Determining agreement between physician claims data and medical chart documentation for polypectomy
Bibliographic record
Abstract
Background: Population level data on polypectomy rates may be useful in examining colorectal cancer (CRC) screening programs.Objectives: To determine level and predictors of agreement between physician claims database procedure code for polypectomy and polypectomy documentation in the endoscopy report.Methods: A retrospective cohort study of patients aged 50 to 80 years who underwent colonoscopy in Montreal was conducted.Physician claims records for the procedure code 0749 (polypectomy) were obtained from the Régie de l'Assurance Maladie du Québec (RAMQ).Accuracy of the RAMQ database was assessed using the endoscopy report polypectomy recording as the gold standard.Results: Polypectomy procedure code in the RAMQ database had the following sensitivity: 84.7% [95% CI=79-89)], specificity: 99.0% [95% CI=98-100)], concordance: 95.1% [95% CI (93, 97)], and kappa statistic: 0.87 [95% CI (0.83, 0.91)].No meaningful predictors of agreement were found.Conclusions: This study supports the use of physician claims databases as accurate sources of data in Quebec for identifying patients undergoing polypectomies.
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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 | 0.020 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".