Reply to “Critical analysis of the study from Reiner et al. on agreement of medical record abstraction and self‐report of breast cancer treatment”
Bibliographic record
Abstract
We thank Wang and colleagues for their interest 1 in our article 2 and their sentiment that our research provides valuable insights into the agreement between medical record abstraction (MRA) and self-reported breast cancer treatment data.We would like to respond to their points.We are surprised at their criticism of our decision to use MRA as the gold standard in our study.Wang et al. acknowledged that medical records are often considered accurate but suggested that incorporating multiple data sources might be considered including direct verification with healthcare providers.1 Epidemiologic studies have historically relied upon MRA with trained medical record abstractors as the gold standard in obtaining information pertaining to cancer treatment.3,4 We are following suit.Notably, in our study, the MRA was conducted through multiple healthcare providers across different specialties, including information obtained directly from doctor's offices and oncology clinics based on treatment locations provided by study participants through direct interviews as well as from radiation oncology clinic notes, an approach that exceeded typical MRA.Furthermore, no cancer
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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 | 0.070 | 0.423 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.031 | 0.036 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".