Fit-For-Purpose PD-L1 Biomarker Testing For Patient Selection in Immuno-Oncology: Guidelines For Clinical Laboratories From the Canadian Association of Pathologists-Association Canadienne Des Pathologistes (CAP-ACP)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since 2014, programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) checkpoint inhibitors have been approved by various regulatory agencies for the treatment of multiple cancers including melanoma, lung cancer, urothelial carcinoma, renal cell carcinoma, head and neck cancer, classical Hodgkin lymphoma, colorectal cancer, gastroesophageal cancer, hepatocellular cancer, and other solid tumors. Of these approved drug/disease combinations, a subset also has regulatory agency-approved, commercially available companion/complementary diagnostic assays that were clinically validated using data from their corresponding clinical trials. The objective of this document is to provide evidence-based guidance to assist clinical laboratories in establishing fit-for-purpose PD-L1 biomarker assays that can accurately identify patients with specific tumor types who may respond to specific approved immuno-oncology therapies targeting the PD-1/PD-L1 checkpoint. These recommendations are issued as 38 Guideline Statements that address (i) assay development for surgical pathology and cytopathology specimens, (ii) reporting elements, and (iii) quality assurance (including validation/verification, internal quality assurance, and external quality assurance). The intent of this work is to provide recommendations that are relevant to any tumor type, are universally applicable and can be implemented by any clinical immunohistochemistry laboratory performing predictive PD-L1 immunohistochemistry testing.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it