Discordance in PD-L1 scores on repeat testing of non-small cell lung carcinomas
Bibliographic record
Abstract
INTRODUCTION: PD-L1 expression may be used as a biomarker predictive of non-small cell lung carcinoma (NSCLC) response to PD-L1 inhibitor treatment. Spatial and temporal heterogeneity in PD-L1 expression and variation in PD-L1 test interpretation may contribute to differences in PD-L1 test results between samples of the same patient's disease. METHODS: Retrospective chart review identified 77 NSCLC patients with 22C3 PharmDx PD-L1 assays performed on two different tumor samples. Patients clinically suspected to have two separate primaries were excluded. PD-L1 test results in different score categories (<1%, 1-49% and ≥50%) were considered discordant. Clinical and pathologic factors associated with discordance were assessed. RESULTS: 28 (36%) of the 77 cases had discordant PD-L1 scores between samples. Patients with an initial test result of 1-49% were most likely to have a discordant second test result. Specimen type (cytology, small biopsy or resection), specimen site (lung, lymph node, pleura/pleural effusion or distant metastasis), time between specimen collection, and treatment between specimen collection were not significantly associated with the rate of discordance. CONCLUSIONS: Repeat PD-L1 testing of the same patient's NSCLC results frequently resulted in discordant test results, independent of whether the samples differed in clinical or pathologic factors. This discordance rate underscores the extent to which PD-L1 levels are heterogeneous and difficult to accurately represent with a single test value. Further study of the predictive value of PD-L1 scores in cases with discordant results is needed.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".