Can We Use Administrative Data to Accurately Identify Patients Who Receive a Prostate Biopsy?
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.
Bibliographic record
Abstract
PURPOSE: Administrative health data can be a valuable resource for health research. Because these data are not collected for research purposes, it is imperative that the accuracy of codes used to identify patients, exposures, and outcomes is measured. PATIENTS AND METHODS: Code sensitivity was determined by identifying a cohort of men with histologically confirmed prostate cancer in the Ontario Cancer Registry and linking them to the Ontario Health Insurance Plan (OHIP) to determine whether a prostate biopsy code had been claimed. Code specificity was estimated using a random sample of patients at The Ottawa Hospital for whom a prostate biopsy code was submitted to OHIP. A simulation model, which varied the code false-positive rate, true-negative rate, and proportion of code positives in the population, was created to determine specificity under a range of combinations of these parameters. RESULTS: Between 1991 and 2012, 97,369 of 148,669 men with histologically confirmed prostate cancer in the Ontario Cancer Registry had a prostate biopsy code in OHIP within 1 week of their diagnosis (code sensitivity, 86.0%). This increased significantly over time (63.8% in 1991 to 87.9% in 2012). The false-positive rate of the code for index prostate biopsies was 1.9%. The simulation model found that the code specificity exceeded 95% for first prostate biopsy but was lower for secondary biopsies because of more false positives. False positives primarily were related to placement of fiducial markers for patients who received radiotherapy. CONCLUSION: Administrative data in Ontario can accurately identify men who receive a prostate biopsy. The code is less accurate for secondary biopsy procedures and their sequelae.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it