The Impact of Hospital Discharge Linkage on Case Ascertainment of Brain Tumors in the Alberta Cancer Registry, 2010-2015.
Bibliographic record
Abstract
BACKGROUND: Concern has been raised regarding the underreporting of nonmalignant central nervous system tumors. This study addressed this issue with 2 objectives: (1) evaluate the impact of linkage with hospital discharges, as recorded in the Discharge Abstract Database (DAD), on supplementing case ascertainment for brain tumors, and (2) identify potential barriers for initial registration of brain tumors in the Alberta Cancer Registry. METHODS: All patients with a brain tumor diagnosed and residing in Alberta from 2010 to 2015 were extracted, after the DAD review, from the Alberta Cancer Registry (ACR). Descriptive statistics were compiled by behavior and type of registration (originally registered or identified through DAD). The total number of expected nonmalignant brain tumors was estimated by applying the Central Brain Tumor Registry of the United States (CBTRUS) incidence rates to the Alberta population and this estimate was compared to observed numbers. Phi coefficients and χ2 tests for the homogeneity of proportions were conducted to examine bivariate relationships of the characteristics of interest. Multiple logistic regression was used to summarize the independent effects on the probability of being identified through DAD. RESULTS: The results show 5% of malignant and 35% of nonmalignant brain tumors were identified through DAD review. When comparing observed to expected number of nonmalignant cases after DAD review, the ACR ultimately captured 76% of those expected. Identification through DAD was statistically significantly (P ≤ .05) associated with patients over 75 years old at diagnosis (odds ratio [OR], 2.5), tumors of benign behavior (OR, 2.6), location at diagnosis in Northern Alberta (OR, 1.5), nonmicroscopically confirmed tumors (OR, 1.3), no visit to a CancerControl Alberta facility (OR, 8.7) and certain histological subtypes, including cranial and spinal nerve tumors (OR, 1.7). CONCLUSION: The use of hospital discharge data significantly improved nonmalignant brain tumor case ascertainment. Therefore, it is recommended that such reviews be instituted annually in provinces while other techniques (such as reminder letters used in Norway or linkages with radiology or other administrative databases) for improving case ascertainment are explored. Those characteristics identified as potential barriers to registration should be investigated to identify possible process improvements in Alberta.
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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 | 0.048 | 0.181 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| 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".