EPCT-11. RURALITY INDEX SCORE AND PEDIATRIC NEURO-ONCOLOGICAL OUTCOME IN ONTARIO
Bibliographic record
Abstract
Abstract Introduction Rapid access to neurosurgical decisions and definitive management are vital for the outcome of neurocritical patients. There are increased challenges of providing services and to maintain critical infrastructure for rural citizens. The relationship between rurality, marginalization and health outcomes has been identified as associated with higher mortality rates and higher rates of many diseases[G1]. Methods Employing linked administrative databases, we retrospectively analyzed a population based cohort of patients diagnosed with a pediatric brain tumour between 1996 to 2017 in Ontario, Canada. The Ontario Marginalization Index was employed as a surrogate for rurality providing an overall Rurality Index for Ontario (RIO) in addition to the 2016 Ontario Marginalization Index (ON-MARG). Results Of 1457 patients included, 54.0% were male, 277 of whom were diagnosed in infancy (i.e., < 3 years of age). Income quintile was evenly distributed with 11.5% classified as living in a rural area of Ontario. The median[G2] distance to the nearest pediatric neurosurgical hospital was 59.6km. The rurality index score (RIO) was 0 in 38.8% of children with the majority of patients with a RIO score of <39. The ON-MARG identified 51.9% of patients living in communities with low concentration of individuals without income from employment. A higher RIO score was not a significant factor (Continuous p=0.092/Ordinal p=0.20) associated with length[G3] of follow up, indicating rurality was not a significant factor for determining compliance to[G4] clinical follow-up. However, a trend towards reduced follow-up compliance in the higher RIO score cohort was identified. Conclusion Rurality and social determinants of health of the region pediatric neuro-oncological patients reside were not associated with patient outcome but a trend towards lower follow-up compliance was identified when children were from regions with RIO>39. Implementation of telehealth follow-up for these patients may overcome barrier to clinical follow-up.[G5]
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".