MétaCan
Menu
Back to cohort
Record W2411643652 · doi:10.1097/pap.0000000000000094

Principles of Analytic Validation of Clinical Immunohistochemistry Assays

2015· review· en· W2411643652 on OpenAlexaff
Jeffrey D. Goldsmith, Patrick L. Fitzgibbons, Paul E. Swanson

Bibliographic record

VenueAdvances in Anatomic Pathology · 2015
Typereview
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsGuidelineImmunohistochemistryMedicinePathologyClinical biochemistryMedical physics

Abstract

fetched live from OpenAlex

All assays performed in anatomic and clinical pathology laboratories must be validated before they are placed into clinical service. This review summarizes strategies for validation of clinical immunohistochemistry assays, and is chiefly based on the recently released guideline released by The College of American Pathologists.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.051
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.051
Threshold uncertainty score0.269

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.054
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.004
Science and technology studies0.0010.008
Scholarly communication0.0060.004
Open science0.0050.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.104
GPT teacher head0.463
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Anatomic PathologySame topicAI in cancer detectionFrench-language works237,207