EPID-08. FINDING THE NEEDLE IN THE HAY STACK – POPULATION-BASED STUDY OF PREDIAGNOSTIC SYMPTOMATIC INTERVAL IN CHILDREN WITH CNS TUMORS
Bibliographic record
Abstract
Abstract PURPOSE Delay in diagnosis of central nervous system (CNS) tumors in children is well documented. The aims of this study were to characterize the symptomatology of CNS tumors and the time to diagnosis in a large pediatric hospital in Canada. METHODS Retrospective chart review of children diagnosed with a CNS tumor between 2000 and 2016 in Vancouver, British Columbia, Canada was performed. Data collected included demographics, symptomatology, tumor type, age at diagnosis, known visits to healthcare professionals, neuroimaging, therapy and post treatment relapse or progression. RESULTS 148 children with complete medical records were reviewed. The average age at diagnosis was 87.8 months (standard deviation (SD) = 59.7; median = 72). 50.7% of patients had posterior fossa tumors and 49.3% had supratentorial tumors. 30% of patients were diagnosed after a single visit to a health care provider. 7.7% of children needed more than 4 visits. Median total time to diagnosis (PSI) was 62 days (range = 0-2047 days). The longest prediagnostic interval was first symptom onset to first healthcare provider visit (PSI1, median 37 days). Patients with posterior fossa tumors, presence of metastases, and symptoms of ataxia and paresis were associated with shorter PSI. CONCLUSIONS CNS tumors in children continue to pose a diagnostic challenge with significant variability in time to diagnosis. Our population-based study found that median time from symptoms to seeking medical advice by parents was over a month. It is essential to uncover the reasons for delay and address them where possible.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".