CALTAG CD3/CD19/CD45 is a fluorescent reagent containing a combination of CD3, CD19 and CD45 monoclonal antibodies conjugated to fluorescein, phycoerythrin and the tandem fluorochrome PE-Cy5, respectively. This reagent permits the simultaneous identification of CD3+ mature T lymphocytes, CD19+ mature B lymphocytes, and CD45+ leukocytes including lymphocytes, monocytes and granulocytes, by flow cytometric methods.
Device Story
Device is a fluorescent monoclonal antibody cocktail (CD3-FITC, CD19-R-PE, CD45-TRI-COLOR) for flow cytometry. Input: peripheral blood leukocytes. Process: incubation with antibodies; lysis of red blood cells; washing; fixation. Principle: antibodies bind specific cell surface antigens (CD3, CD19, CD45). Output: fluorescently labeled cells analyzed by flow cytometer. Used in clinical laboratories by technicians/pathologists. Output allows identification/quantification of lymphocyte subsets; aids in immunophenotyping and clinical assessment of immune status.
Clinical Evidence
Bench testing only. Study of 155 healthy donors (ages 16-72) established expected values for CD3, CD19, and CD45 components. Correlation studies (n=175) compared the combination reagent against single-antibody reagents, showing high correlation (r² 0.948-0.989) and slopes near 1.0. Specificity data provided across ethnic groups (Caucasian, Black, Hispanic, Oriental) confirming consistent staining of target leukocyte populations.
Technological Characteristics
Mouse monoclonal antibody combination conjugated to FITC, R-PE, and PE-Cy5 (TRI-COLOR). Liquid form in PBS. Analyzed via flow cytometry. No specific materials or software algorithms described; relies on standard immunofluorescence and flow cytometric gating principles.
Indications for Use
Indicated for the simultaneous identification of CD3+ mature T lymphocytes, CD19+ mature B lymphocytes, and CD45+ leukocytes (lymphocytes, monocytes, granulocytes) in human peripheral blood samples via flow cytometry.
Regulatory Classification
Identification
An automated differential cell counter is a device used to identify one or more of the formed elements of the blood. The device may also have the capability to flag, count, or classify immature or abnormal hematopoietic cells of the blood, bone marrow, or other body fluids. These devices may combine an electronic particle counting method, optical method, or a flow cytometric method utilizing monoclonal CD (cluster designation) markers. The device includes accessory CD markers.
Special Controls
*Classification.* Class II (special controls). The special control for this device is the FDA document entitled “Class II Special Controls Guidance Document: Premarket Notifications for Automated Differential Cell Counters for Immature or Abnormal Blood Cells; Final Guidance for Industry and FDA.”
Predicate Devices
Caltag CD3 FITC single monoclonal antibody
Caltag CD19 R-PE single monoclonal antibody
Coulter CD19 RD1 single monoclonal antibody
Submission Summary (Full Text)
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CALTAG LABORATORIES
JAN 17 1997
K964856
510(K) SUMMARY
SUMMARY OF SAFETY AND EFFECTIVENESS DATA
CD3 FITC/CD19 R-PE/CD45 TRI-COLOR™
Mouse Monoclonal Antibody Combination
To Human Cell Surface Antigens by Flow Cytometry
NAME AND LOCATION OF MANUFACTURER:
Caltag Laboratories, Inc.
1849 Old Bayshore Highway
Suite 200
Burlingame, CA 94010
(800) 874-4007
NAME OF CONTACT PERSON:
Robert C. Johnson
Executive Vice President
Caltag Laboratories, Inc.
DATE OF PREPARATION OF SUMMARY:
December 1, 1996
Caltag Laboratories Inc. • 1849 Bayshore Blvd., Suite #200 • Burlingame, CA 94010
Telephone (415) 652-0468 • (800) 874-4007 • FAX (415) 652-9030
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## TRADE NAME OF THE DEVICE:
CD3 FITC/CD19 R-PE/CD45 TRI-COLOR™
Mouse Monoclonal Antibody Combination
To Human Cell Surface Antigens by Flow Cytometry
## COMMON NAME:
Caltag CD3 FITC/CD19 R-PE/CD45 TRI-COLOR™ Monoclonal Antibody Combination
## CLASSIFICATION NAME:
Automated Differential Cell Coulter (21 CFR 864.5220)
## LEGALLY MARKETED DEVICE (PREDICATE DEVICE) TO WHICH THE MANUFACTURER IS CLAIMING SUBSTANTIAL EQUIVALENCE:
The Caltag CD3 FITC monoclonal antibody in the CD3 FITC/CD19 R-PE/CD45 TRI-COLOR combination is substantially equivalent to the Caltag CD3 FITC single monoclonal antibody for in vitro diagnostic use.
The Caltag CD19 R-PE monoclonal antibody in the CD3 FITC/CD19 R-PE/CD45 TRI-COLOR combination is substantially equivalent to the Caltag CD19 R-PE single monoclonal antibody for in vitro diagnostic use, and is substantially equivalent to the Coulter CD19 RD1 single monoclonal antibody for in vitro diagnostic use.
## DESCRIPTION OF THE DEVICE:
The CALTAG CD3/CD19/CD45 monoclonal antibody combination binds to the surfaces of viable blood cells that express the corresponding antigens. To identify cells bearing these antigenic determinants, peripheral blood leukocytes are incubated with the monoclonal antibody, and washed to remove unbound antibody. Prior to removal of unbound antibody, lysis solution is added to lyse red blood cells. An appropriate fixative solution is added to lysed and washed cells. Stained and fixed cells are subsequently analyzed by flow cytometric methods.
## INTENDED USE OF THE DEVICE:
CALTAG CD3/CD19/CD45 is a fluorescent reagent containing a combination of CD3, CD19 and CD45 monoclonal antibodies conjugated to fluorescein, phycoerythrin and the tandem fluorochrome PE-Cy5, respectively. This reagent permits the simultaneous identification of CD3+ mature T lymphocytes, CD19+ mature B lymphocytes, and CD45+ leukocytes including lymphocytes, monocytes and granulocytes, by flow cytometric methods.
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SUMMARY OF THE TECHNICAL CHARACTERISTICS OF THE MANUFACTURER'S DEVICE COMPARED TO THE PREDICATE DEVICE:
Comparisons of Caltag Monoclonal Antibody Components to Caltag and Coulter Single Monoclonal Antibodies
| No. | Item | Caltag Components | Caltag Antibodies
Coulter Antibody | Comparison |
| --- | --- | --- | --- | --- |
| 1. | Intended Use | Flow Cytometry | Flow Cytometry
Immunofluorescence | Substantially equivalent |
| 2. | Specificity | CD3 | CD3 Caltag | Substantially equivalent |
| | | CD19 | CD19 Caltag | |
| | | CD19 | CD19 Coulter | |
| | | CD45 | | "live gating" only |
| 3. | Target cell
for CD3 | Mature | Mature | Substantially equivalent |
| | | T Lymphocyte | T Lymphocytes | |
| | for CD19 | B lymphocytes | B lymphocytes | Substantially equivalent |
| | for CD45 | All Leukocytes | All Leukocytes | "live gating" only |
| 4. | Chemical form | Monoclonal antibody | Monoclonal antibody | Substantially equivalent |
| 5. | Fluorochromes | FITC | FITC | Substantially equivalent |
| | | R-PE | R-PE (Caltag) | |
| | | R-PE | RD1 (Coulter) | |
| | | TRI-COLOR | not available (Coulter) | |
| 6. | Available forms | | | |
| | FITC | liquid, PBS | lyophilized | Substantially equivalent |
| | PE | liquid, PBS | liquid, PBS | |
| | TRI-COLOR | liquid, PBS | none available (Coulter) | |
| 7. | Sample prep.
methods | whole blood | whole blood | Substantially equivalent |
NON CLINICAL TESTS SUPPORTING A DETERMINATION OF SUBSTANTIAL EQUIVALENCE:
**EXPECTED VALUE DATA**
Blood samples were collected from a total of 155 apparently healthy normal donors in an age range of 16 to 72, with a mean age of 41, for the determination of expected values of the Caltag CD3/CD19/CD45 monoclonal antibody combination. Samples were collected and analyzed in each of three independent laboratories. An approximately equal number of males and females were collected and analyzed in each laboratory.
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The population contained members of differing ethnic origins, including adult Caucasians, Blacks, Orientals and Hispanics. Donors in geographically diverse areas of the United States, including the Eastern, South Central and Western regions, participated in this study.
Summary of expected values for CALTAG CD3 FITC CD19 R-PE and CD45 TRI-COLOR components for 155 normal donors:
| procedure | mean % positive | S.D. | Range ±2 S.D. |
| --- | --- | --- | --- |
| CD3 FITC component | 72.8 | 7.7 | 57-88 |
| CD19 R-PE component | 13.3 | 4.5 | 4-22 |
| CD45 TRI-COLOR component | 100.0 | 0.1 | 100-100 |
Comparison of CALTAG CD3 FITC, CD19 R-PE and CD45 TRI-COLOR components to the CALTAG CD3 FITC, CD19 R-PE and CD45 TRI-COLOR single antibodies. Blood samples were collected from different randomly selected populations of adult normal donors having similar age, gender and ethnic distributions for the determination of expected values for the Caltag CD3 FITC and CD45 TRI-COLOR single monoclonal antibodies, and from the same population of adult normal donors for the determination of expected values for Caltag CD19 R-PE single antibody.
### SINGLE ANTIBODIES
| procedure | mean % positive | S.D. | Range ±2 S.D. | n |
| --- | --- | --- | --- | --- |
| CD3 FITC | 71.8 | 6.9 | 58-86 | 130 |
| CD19 R-PE | 13.0 | 4.2 | 5-21 | 155 |
| CD45 TRI-COLOR | 99.0 | 0.9 | 97-100 | 40 |
Expected values for pediatrics and adolescents have not been determined.
The values obtained from normal individuals may vary from laboratory to laboratory; therefore, it is recommended that each laboratory establish its own normal range.
## SPECIFICITY DATA
Blood samples were obtained from healthy normal donors of Caucasian, Black, Hispanic and Oriental ethnic origins. Samples of each donor were stained with CALTAG CD3 FITC/CD19 R-PE/CD45 TRI-COLOR monoclonal antibody combination. Cells contained in the lymphocyte, monocyte and granulocyte regions were selected for analysis. Separate samples from the same donors were prepared for analysis of red blood cells and platelets and stained with each of the CALTAG monoclonal antibodies.
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| CD3 FITC Component | | | | | |
| --- | --- | --- | --- | --- | --- |
| Ethnic | Percent of Stained Cells | | | | |
| Origin | Lymph. | Mono. | Gran. | Plt. | RBC |
| Caucasian | 65.2 | 1.7 | 1.5 | 0.6 | 0.3 |
| Caucasian | 81.4 | 1.4 | 0.5 | 0.3 | 0.4 |
| Hispanic | 79.2 | 1.9 | 0.6 | 0.3 | 0.4 |
| Oriental | 81.2 | 1.3 | 0.9 | 0.4 | 0.2 |
| Black | 84.9 | 0.9 | 0.6 | 0.4 | 0.4 |
| Mean | 78.4 | 1.4 | 0.8 | 0.4 | 0.3 |
| ±1 S.D. | 7.6 | 0.4 | 0.4 | 0.1 | 0.1 |
| CD19 R-PE Component | | | | | |
| --- | --- | --- | --- | --- | --- |
| Ethnic | Percent of Stained Cells | | | | |
| Origin | Lymph. | Mono. | Gran. | Plt. | RBC |
| Caucasian | 18.1 | 1.3 | 0.5 | 0.4 | 0.2 |
| Caucasian | 12.9 | 0.6 | 0.4 | 0.2 | 0.2 |
| Hispanic | 13.4 | 0.5 | 0.6 | 0.2 | 0.6 |
| Oriental | 12.1 | 0.8 | 0.5 | 0.3 | 0.3 |
| Black | 12.7 | 0.9 | 0.3 | 0.4 | 0.2 |
| Mean | 13.8 | 0.8 | 0.5 | 0.3 | 0.3 |
| ±1 S.D. | 2.4 | 0.3 | 0.1 | 0.1 | 0.2 |
| CD45 TRI-COLOR Component | | | | | |
| --- | --- | --- | --- | --- | --- |
| Ethnic | Percent of Stained Cells | | | | |
| Origin | Lymph. | Mono. | Gran. | Plt. | RBC |
| Caucasian | 100.0 | 100.0 | 100.0 | 0.8 | 0.2 |
| Caucasian | 100.0 | 100.0 | 100.0 | 0.5 | 0.3 |
| Hispanic | 100.0 | 100.0 | 100.0 | 0.3 | 0.4 |
| Oriental | 100.0 | 100.0 | 100.0 | 0.4 | 0.3 |
| Black | 100.0 | 100.0 | 100.0 | 0.3 | 0.2 |
| Mean | 100.0 | 100.0 | 100.0 | 0.5 | 0.3 |
| ±1 S.D. | 0.0 | 0.0 | 0.0 | 0.2 | 0.1 |
Specific and/or nonspecific antibody Fc binding to monocytes in a patient sample can be excluded by proper gating on lymphocytes on the flow cytometer.
## CLINICAL TESTS SUPPORTING A DETERMINATION OF SUBSTANTIAL EQUIVALENCE:
### CORRELATION DATA
The Correlation study was performed on 175 donors, including 155 normal and 20 abnormal donors.
Comparison of the CALTAG CD3 FITC conjugated monoclonal antibody component of CD3 FITC/CD19 R-PE/CD45 TRI-COLOR with the CALTAG CD3 FITC conjugated single monoclonal antibody:
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| procedure | mean % positive | r² value | slope | Y intercept |
| --- | --- | --- | --- | --- |
| CD3 FITC component | 69.7 | 94.8 | 1.01 | 0.33 |
| CD3 FITC single | 68.3 | | | |
CD3 FITC component
Slope + 1.01
y intercept + 0.33
Linear regression y = 0.33 + 1.01x
Comparison of the CALTAG CD19 R-PE conjugated monoclonal antibody component of CD3 FITC/CD19 R-PE/CD45 TRI-COLOR with the Caltag CD19 R-PE conjugated single monoclonal antibody:
| procedure | mean % positive | r² value | slope | Y intercept |
| --- | --- | --- | --- | --- |
| CD19 R-PE component | 16.8 | 98.9 | 1.02 | 0.08 |
| CD19 R-PE single | 16.4 | | | |
CD19 R-PE component
Slope + 1.02
y intercept + 0.08
Linear regression y = 0.08 + 1.02x
Comparison of the CALTAG CD19 R-PE conjugated monoclonal antibody component of CD3 FITC/CD19 R-PE/CD45 TRI-COLOR with the Coulter CD19 RD1 conjugated single monoclonal antibody:
| procedure | mean % positive | r² value | slope | Y intercept |
| --- | --- | --- | --- | --- |
| CD19 R-PE component | 16.8 | 98.1 | 0.95 | 1.29 |
| Coulter CD19 RD1 | 16.2 | | | |
CD19 R-PE component
Slope + 0.95
y intercept + 1.29
Linear regression y = 1.29 + 0.95x
# BIBLIOGRAPHY
1. Hsu S., Cossman J., Jaffe E.: Lymphocyte subsets in normal human lymphoid tissues. Am. J. Clin. Path. 80:21-30, 1983.
2. Morimoto C., Letvin N.L., Distaso J.A., et al: The cellular basis for the induction of antigen-specific T8-suppressor cells. Eur. J. Immunol. 16:198-204, 1986.
3. Kurle R., Cluster Report: CD3, in Fourth International Workshop And Conference On Human Leukocyte Differentiation Antigens, pp 290-293, Vienna, 1989.
4. Transy C., Moingeon P.E., Marshall B., et al: Most murine anti-human CD3 mAb recognize the human CD3 epsilon subunit. in Fourth International Leukocyte Workshop and Conference On Human Leukocyte Differentiation Antigens. pp 293-295, Vienna, 1989.
5. Dorken B., Moller P., Pezzutto A., Schwartz-Albiez R., Moldenhauer G., B-cell antigens:CD19, in Fourth International Workshop and Conference On Human Leucocyte Differentiation Antigens, pp 34-36, Vienna, 1989.
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6. Pirruccello S.J., Johnson D.R., Reagents for flow cytometry: Monoclonal antibodies and hematopoietic cell antigens, in Flow Cytometry And Clinical Diagnosis, pp 56-78, pub. American Society of Clinical Pathologists, 1994.
7. Shields J.G., Rigley K.P., Callard R.E., Regulation of human B cell proliferation by the CD19 cell-surface glycoprotein, in Fourth International Workshop and Conference On Human Leucocyte Differentiation Antigens, pp 40-43, Vienna, 1989.
8. Schwinzer R., Cluster Report CD45/CD45R, in Fourth International Workshop and Conference On Human Leucocyte Differentiation Antigens, pp 628-634, Vienna, 1989.
9. Fifth International Workshop and Conference On Human Leucocyte Differentiation Antigens, Leucocyte Typing V, White Cell Differentiation Antigens, Boston, 1993.
10. Reinherz E.L., Cooper M.D., Schlossman S.F., Abnormalities of T cell maturation and regulation in human beings with immunodeficiency disorders. J. Clin. Invest. 68:609-705, 1981.
11. Schmidt R.E., Monoclonal antibodies for diagnosis of immunodeficiencies. Blut 59:200-206, 1989.
12. Smolen J.S., Morimoto C., Steinberg A.D., et al: Systemic lupus erythematosus: delineation of subpopulations by clinical serologic and T cell marker analysis. Am. J. Med. Sci. 289:139-141, 1985.
13. Morimoto C., Steinberg A.D., Letvin A.L., et al: A defect of immunoregulatory T cell subsets in SLE patients demonstrated with anti-2H4. J. Clin. Invest. 79:762-765, 1987.
14. Duke O., Panayi G.S., Janossy G., et al: Analysis of T cell subsets in the peripheral blood and synovial fluid of patients with rheumatoid arthritis by means of monoclonal antibodies. Ann Rheum Dis. 42:357-364, 1983.
15. Reinherz E.L., O'Brien C., Rosenthal P., et al: The cellular basis for viral-induced immunodeficiency: Analysis by monoclonal antibodies. J. Immunol. 125:1266-1274, 1980.
16. Nadler L.M., in Leukocyte Typing II, Volume 2, B cell/Leukemia Panel Workshop: Summary and Comments, Chapter 1:2-20, Springer Verlag, New York, 1986.
17. Wedgwood R.J., X-linked agammaglobulinemia, in CRC Handbook Series In Clinical Laboratory Science, CRC Press, West Palm Beach Florida, pp 41-50, 1978.
18. Pearl E.R., Vogler L.B., Okos A.J. et al, B lymphocyte precursors in bone marrow. An analysis of normal individuals and patients with antibody-deficiency states, J. Immunol. 120:1169-175, 1978.
19. Spickett G.P., Webster A.D., Farrant J., Cellular abnormalities in common variable immunodeficiency, Immunodefic. Rev. 2:199-219, 1990.
20. Small T.N., Keever C., Collins N. et al, Characterization of B cells in severe combined immunodeficiency disease, Hum. Immunol. 25:181-193, 1991.
21. Goldstein R., Izaguirre L.C., Douglas J. et al, Systemic lupus erythematosus and common variable panhypogammaglobulinemia. Theoretical and practical considerations, Fed. Proc. 29:1606-1611, 1985.
22. Schwinzer R., Cluster Report CD45/CD45R, in Fourth International Workshop and Conference On Human Leucocyte Differentiation Antigens, pp 628-634, Vienna, 1989.
23. Loken M.L., Brosnan N.N., Back, B.A., Ault K.A.: Establishing optimal lymphocyte gates for immunophenotyping for flow cytometry, Cytometry 11:453-459, 1990.
24. Mandy F.F., Bergeron M., Recktenwald D., Izaguirre C.A., A simultaneous three color T cell subsets analysis with single laser flow cytometers using T cell gating protocol, J. Immunol. Methods 156:151-162, 1992.
25. Stelzer G.T., Schults K.S., Loken M.R., CD45 gating for routine flow cytometric analysis of human bone marrow specimens, in Clinical Flow Cytometry, A. Landay, K. Ault, K. Bauer, P. Rabinovitch, eds, pp 265-280, 1993.
26. Stewart C.C., Stewart S.J., Multiparameter analysis of leukocytes by flow cytometry, in Methods Of Cell Biology, Z. Darzynkiewicz, H. Crissman eds, in press, 1994.
27. 1994 Revised Guidelines for the Performance of CD4 + T-Cell Determinations in Persons with Human Immunodeficiency Virus Infection, Morbidity And Mortality Weekly Report (MMWR), Volume 43/No. RR-3, March 4, 1994.
28. Nicholson J.K.A., Green T.A. and Collaborative Laboratories, Selection of anticoagulants for lymphocyte immunophenotyping. J. Immunol. Methods 165:31-35, 1993.
29. Tennant J.R., Evaluation of the Trypan Blue technique for determination of cell viability, Transplantation 2:685-694, 1964.
30. Koepke J.A., Landay A.L.: Precision and Accuracy of Absolute Lymphocyte Counts, Clin. Immunol. and Immunopath. 52:19-27, 1989.
31. Brown M.C., Hoffman R.A., Kirchanski S., Controls for flow cytometers in hematology and cellular immunology, Ann. N.Y. Acad. Sci. 468:93-103, 1986.
32. Durrand R.E., Calibration of flow cytometer detector systems, Cytometry 2:192-193, 1981.
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Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
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Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
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Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
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Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
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Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
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With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
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Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
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Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.