Misclassification of colorectal cancer stage and area variation in survival
Bibliographic record
Abstract
We previously investigated the impact of health area of residence on colon and rectal cancer survival by estimating area-specific relative excess risk of death (RER), stratified by stage at diagnosis. The aims of this study were to quantify errors in colorectal cancer stage obtained from an Australian population-based cancer registry and assess the potential impact of errors in stage on these estimates. For a subset of cases, we compared the cancer registry stage with that from a survey of treating surgeons. We then randomly reallocated all cases to a simulated "corrected" stage according to the estimated misclassification probabilities and repeated the analysis of area variation stratified by simulated stage 1,000 times. We found 70% agreement between the Registry and Survey stage. This reallocation of the Registry cases by stage resulted in substantial variation in area-specific RERs across the simulated samples. Area variation in survival for localized colon and localized rectal cancer, which were previously statistically significant when classified using Registry stage, appeared no longer to be so. Misclassification of cancer registry stage can have an important impact on estimates of spatial variation in stage-specific colon and rectal cancer survival. If population-based cancer registry data are to be effectively used in evaluating and improving cancer care, the quality of the stage data may need to be improved.
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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 | 0.026 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".