EPID-28. WHAT DO PATTERNS OF “UNCLASSIFIED” BRAIN TUMORS TELL US ABOUT INCIDENCE RATES OF NON-MALIGNANT BRAIN TUMORS IN CANADA?
Bibliographic record
Abstract
Abstract BACKGROUND Patients with non-malignant brain tumors (NMBT) can experience significant morbidity and mortality; the degree of which varies by histological subtype. A recent report on all primary brain tumors in Alberta (AB), British Columbia (BC), Manitoba (MB) and Ontario (ON) showed 19.1% of NMBT were categorized as ‘unclassified’. Missing data on histological subtypes encumbers efforts to understand the burden of disease associated with NMBT. The purpose of this analysis was to examine the distribution of unclassified tumors across participating provinces to identify targets for improved surveillance. METHODS Data were provided from cancer registries in AB, BC,MB, and ON. Age Standardized Incidence Rates (ASIR) and frequencies were estimated using SAS 9.4. Given data from the United States (U.S) is accurate and the population is comparable, ASIR of NMBT in the U.S were used as expected values in this analysis. The 2011 Canadian Population and the 2000 US population were used for standardization to compare within Canada and to U.S data respectively. RESULTS The ASIR of total NMBT and was highest in ON, relative to other participating provinces and similar to that of the U.S (15.25 and 15.91 cases/100,000 person-years, respectively). However, ON had an ASIR about 10 times higher than other provinces for unclassified tumors (ON:6.3 cases/100,000 person-years; BC: 0.4 cases/100,000 person-years; AB:0.3 cases/100,000 person-years; and MB:0.6 cases/100,000 person-years). When unclassified tumors were excluded from the analysis, the ASIR of NMBT was similar across participating provinces. CONCLUSIONS Findings from this analysis indicate that the Ontario provincial cancer registry has complete data on total NMBT cases. However, the high ASIR of unclassified cases in the NMBT category indicates that quality control measures could be improved. Further examination of the unclassified cases in ON could be used to improve the accuracy of the ON cancer registry and the completeness of other province’s registries.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".