Reducing DCO registrations through electronic matching of cancer registry data and routine hospital data
Bibliographic record
Abstract
The Thames Cancer Registry (TCR) has registered a high proportion of tumours from death certificate information only (DCO) registrations. This paper describes the results of a study set up to establish whether this proportion could be reduced by linking cancer registrations with routine hospital data from the Hospital Episodes Statistics (HES) data set using computerized matching. A total of 67752 registrations were identified from the TCR. Matches were found in the HES data set for 66%. The proportion of cases retrieved for each tumour site was: 72% for colorectal cancer; 62% for cancer of the lung, trachea or bronchus; and 65% for female breast cancer. For all three tumour sites the proportion of matches found for patients registered from hospital case notes was higher than the proportion found for patients registered as DCOs (P < 0.0001 for all three tumour sites). Among matched DCO cases, 58% had at least one procedure recorded. DCO rates might be reduced by as much as 43% (from 17% of total registrations to less than 10%) for the three most common cancers if the method of electronic matching outlined here was used. Younger age groups, prognosis of tumour site and residence in North Thames region were all positively associated with successful matching (P < 0.0001 in all three cases). Many matched DCO cases were found to have had more than one admission for cancer. Among ordinary in-patient admissions, admissions to patients ratios of 1.5, 1.4 and 1.9 were found for colorectal, lung and breast cancers respectively. Of 5190 matched DCOs a procedure was recorded for 3013 (58%). HES data offer a useful aid to follow-up of case notes on patients identified to the registry by death certificates. Doubts about the completeness and accuracy of HES data mean case notes must remain the 'gold standard'.
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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 | 0.042 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".