Sensitivity and specificity of self-reported cancer history compared to cancer registry.
Bibliographic record
Abstract
234 Background: The Ontario Health Study (OHS) is a large prospective epidemiologic cohort study in which any Ontario resident eighteen years of older may enroll regardless of prior medical history. Baseline data are collected using web-based tools. As part of the consent process, participants are asked for consent to link study data with administrative and health care claims databases, including the Ontario Cancer Registry (OCR). There is an option to enter their Health Insurance Number (HIN) for this purpose. The purpose of this study was to link these data and evaluate the accuracy of self-reported cancer history compared to the cancer registry. Methods: Consenting participants that provided HINs were deterministically linked to the administrative data. Those that did not were probabilistically linked using name, sex and date of birth. Cancer registry records indicating a cancer diagnosed before the date of completion of the OHS baseline survey were considered the gold standard. Concordance, sensitivity, and specificity were assessed. Results: OHS records were successfully linked to administrative claims data and the Ontario Cancer Registry (OCR) with an 85.13% match rate. The final cohort consisted of 139,798 participants. A personal history of cancer was reported by 13,171 of these subjects, out of which 10,066 were found with a record in the OCR. The sensitivity of self-report was 77% and the specificity 95%. Excluding cancers diagnosed after the completion of the baseline survey increased sensitivity of self-report to 93%. The main area of discrepancy causing low sensitivity was the self-reporting of non-melanomatous skin cancers in the OHS questionnaire. Conclusions: While some self-over-reporting of cancer history may occur, cancers with lower metastatic potential tend to be under-captured in our provincial cancer registry. These findings have implications for cohort creation for research and quality improvement.
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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 | 0.021 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".