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Record W4403416696 · doi:10.1093/ajcp/aqae129.319

Impact of a PD-L1 Learning Collaborative: outcomes from a mixed-methods evaluation

2024· article· en· W4403416696 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Tools and Methods
Canadian institutionsAxdev Group (Canada)
Fundersnot available
KeywordsMedicineComputer sciencePsychology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.143
GPT teacher head0.646
Teacher spread0.504 · 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