Finding the Needle in the Hay Stack: Population-based Study of Prediagnostic Symptomatic Interval in Children With CNS Tumors
Bibliographic record
Abstract
Central nervous system (CNS) tumors in children are a devastating diagnosis and delay in diagnosis is well documented in the literature. The aim of this study was to document and characterize time to diagnosis of CNS tumors among children 0 to 17 years of age in a pediatric center. A retrospective chart review was conducted of medical records of children with CNS tumors from 2000 to 2016 in British Columbia, Canada and 148 reports were available for review. Average age at diagnosis was 87.8 months (SD=59.7; median=72). One third (30%) were diagnosed after a single visit to a health care provider and 11 (7.7%) after more than 4 visits. Median time to diagnosis (prediagnostic symptomatic interval [PSI]) was 62 days (average 197±341 d; range, 0 to 2047 d). Longest period was time from first symptom to first health care provider visit (PSI1, median 37 d). Tumors in the posterior fossa and symptoms of ataxia or paresis were associated with a significantly shorter PSI. CNS tumors in children continue to pose a diagnostic challenge with variability in time to diagnosis. Our population-based study suggests variability in time to diagnosis with a need for education of families to identify symptoms associated with CNS tumors.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".