Predictors of diagnostic interval and associations with outcome in acute lymphoblastic leukemia
Bibliographic record
Abstract
BACKGROUND: Little is known about diagnostic interval lengths in childhood cancer, their predictors or impact upon survival. To date, studies have relied on questionnaires or chart abstraction. We aimed to construct and validate a diagnostic interval measure using health services data among children with acute lymphoblastic leukemia (ALL) in order to determine predictors of prolonged intervals and associations with event-free survival (EFS). PROCEDURE: All children with ALL diagnosed 1995-2011 (N = 1,541) in Ontario, Canada were linked to population-based health administrative databases. Healthcare claims prior to diagnosis were used to define healthcare episodes. Diagnostic intervals (time between first episode with diagnostic code a priori classified as consistent with underlying ALL, and diagnosis) were validated by correlation with a chart abstraction-based measure. RESULTS: Intervals were generally short (median 2 days, IQR 1-3). Predictors of longer intervals included having general primary care physicians versus pediatricians (odds ratio 1.60, 95%CI 1.04-2.47; P = 0.03). While prolonged diagnostic intervals were associated with superior EFS (hazard ratio 0.71, 95%CI 0.52-0.98; P = 0.04), this was explained by confounding by disease biology. CONCLUSIONS: Health administrative data can be used to measure diagnostic intervals in ALL and potentially other pediatric malignant and non-malignant diseases. Diagnostic intervals were short and a marker of disease severity rather than independent predictors of outcome. These findings may be used to address caregiver guilt and caution against "early diagnosis" benchmarks not based in evidence. Future studies should examine the impact of diagnostic interval length in other conditions, but should account for potential confounding by disease severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".