Rurality index score and pediatric neuro-oncological outcome in Ontario
Bibliographic record
Abstract
OBJECTIVE: Rapid access to neurosurgical decisions and definitive management are vital for the outcome of neurocritical patients. There are unique challenges associated with the provision of services required to maintain critical infrastructure for rural citizens. Given that a relationship between rurality, marginalization, and health outcomes has been identified as associated with higher mortality rates and higher rates of many diseases, the authors studied whether worse clinical outcomes were associated with rurality in pediatric neuro-oncological disease. METHODS: Using linked administrative databases, the authors retrospectively analyzed a population-based cohort of patients diagnosed with a pediatric brain tumor between 1996 and 2017 in Ontario, Canada. The main variable of interest was the Rurality Index for Ontario (RIO; larger value denotes more rural); the main outcome was survival, while controlling for surgery and tumor type. RESULTS: Of the 1428 patients included, 53.9% were male. Overall survival of all the children (controlling for surgery and tumor type) at 1, 5, and 10 years was 84.7%, 65.1%, and 58.4%, respectively. A total of 11.5% were classified as living in a rural area of Ontario. The distance to the nearest pediatric neurosurgical hospital ranged from 25.6 to 167.4 km. The RIO score was 0 in 38.7% of children, and the majority of patients had a RIO score < 40. A higher RIO score was not a significant factor (continuous p = 0.12/ordinal p = 0.18) associated with length of follow-up, indicating that rurality was not significantly linked to compliance with clinical follow-up. CONCLUSIONS: Rurality of the region in which pediatric neuro-oncological patients reside was not associated with patient outcome (HR 0.83, p = 0.39).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".