The Importance of Cancer Registry Linkage for Studying Rare Cancers in Prospective Cohorts
Bibliographic record
Abstract
Large prospective cohort studies may offer an opportunity to study the etiology and natural history of rare cancers. Cancer diagnoses in observational cohort studies are often self-reported. Little information exists on the validity of self-reported cancer diagnosis, especially rare cancers, in Canada. This study evaluated the validity of self-reported cancer diagnosis in Alberta's Tomorrow Project (ATP), a provincial cohort in Canada. ATP data were linked to the Alberta Cancer Registry (ACR). The first instance of self-reported cancer in a follow-up survey was compared to the first cancer diagnosis in the ACR after enrollment. The sensitivity and positive predictive value (PPV) were estimated for the reporting of cancer status, reporting of common or rare cancer, and reporting of site-specific cancer. Logistic regression analysis explored factors associated with false positive, false negative, and incorrect cancer site reporting. In the 30,843 ATP participants who consented to registry linkage, there were 810 primary cancer diagnoses in the ACR and 959 self-reports of first cancer post-enrollment, for a cancer status sensitivity of 92.1% (95% CI: 90.0-93.9) and PPV of 77.8% (95% CI: 75.0-80.4). Compared to common cancers, rare cancers had a lower sensitivity (62.8% vs. 89.6%) and PPV (35.8% vs. 84.5%). Participants with a rare cancer were more likely to report an incorrect site than those with a common cancer. Rare cancers were less likely to be captured by active follow-up than common cancers. While rare cancer research may be feasible in large cohort studies, registry linkage is necessary to capture rare cancer diagnoses completely and accurately.
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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 | 0.317 | 0.460 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".