Impact of a PD-L1 Learning Collaborative: outcomes from a mixed-methods evaluation
Bibliographic record
Abstract
Abstract Introduction/Objective PD-L1 Immunohistochemistry testing is often required to determine eligibility for immune checkpoint inhibitor therapy. An ASCP PD-L1 Learning Collaborative (LC) was formed aiming to: 1) identify ways to streamline PD-L1 testing; 2) encourage members to locally implement changes; and 3) develop a resource guide for the community. Methods/Case Report The PD-L1 LC (n=38 pathologists and laboratory professionals) participated in 3 activities: 1) 4 meetings in which LC members discussed current literature and practice; 2) 3 30-minute on-demand, credit-bearing panel videos, in which selected LC members summarized the LC outputs and shared their experiences; and 3) a guide summarizing resources relevant to streamlining PD-L1 testing. The mixed-methods evaluation included: 1) five- minute surveys before (n=24), immediately after (n=11) and 7-months post-LC (n=17); 2) polling questions (2-4 per meeting); 3) semi-structured interviews (n=5). Quantitative data was analysed using descriptive and inferential analysis, qualitative data using a thematic analysis / inductive reasoning approach. Results (if a Case Study enter NA) Baseline data confirmed delays in testing caused by unstandardized PD-L1 testing processes and suboptimal confidence in PD-L1 validation and methodologies. Post-LC, members self-reported perceived increased knowledge and higher confidence levels regarding discussion of PD-L1 scientific evidence and best practices. At the 7-month follow-up, 59% of respondents reported at least one PD-L1-related practice change, with 29% of participants selecting:1) Improving protocols for specimen acquisition, handling, or processing; 2) Improving communication with multidisciplinary care team; 3) Optimizing biomarker testing workflows. Remaining suboptimal knowledge post-LC suggests need for further educational efforts. Participants identified “Tumor-specific considerations” as the main resource missing for PD-L1 testing. Conclusion A learning collaborative has shown impact in improving PD-L1 testing processes and related practices among a group of pathology professionals. The group successfully made available three panel videos and a resource guide, and PD-L1-related practice changes were reported. Future initiatives should address remaining gaps and develop tumor-specific PD-L1 testing considerations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.184 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".