Discrepancies in central review re-testing of patients with ER-positive and HER2-negative breast cancer in the OPTIMA prelim randomised clinical trial
Bibliographic record
Abstract
BACKGROUND: There is limited data on results of central re-testing of samples from patients with invasive breast cancer categorised in their local hospital laboratories as oestrogen receptor (ER) positive and human epidermal growth factor receptor homologue 2 (HER2) negative. METHODS: The Optimal Personalised Treatment of early breast cancer usIng Multiparameter Analysis preliminary study (OPTIMA prelim) was the feasibility phase of a randomised controlled trial to validate the use of multiparameter assay-directed chemotherapy decisions in the UK National Health Service (NHS). Eligibility criteria included ER positivity and HER2 negativity. Central re-testing of receptor status was mandatory. RESULTS: Of the 431 patients tested centrally, discrepant results between central and local laboratory results were identified in only 19 (4.4%; 95% confidence interval 2.5-6.3%) patients (with 21 tumours). On central review, seven patients had cancers that were ER-negative (1.6%) and 13 (3.0%) patients with 15 tumours had HER2-positive disease, including one tumour discrepant for both biomarkers. CONCLUSIONS: Central re-testing of receptor status of invasive breast cancers in the UK NHS setting shows a high level of reproducibility in categorising tumours as ER-positive and HER2-negative, and raises questions regarding the cost effectiveness and clinical value of central re-testing in this sub-group of breast cancers in this setting.
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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 | 0.157 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".