Delays in Diagnosis of Pediatric Solid Tumors in Singapore
Bibliographic record
Abstract
OBJECTIVE: To investigate social, clinical, and disease-related factors associated with diagnostic delay. MATERIALS AND METHODS: Two-hundred and 9 solid tumor cases reported to the Singapore Childhood Cancer Registry at KK Hospital between 1997 and 2007 were reviewed retrospectively. The natural logarithms of total delay times were correlated with social, clinical, and disease factors using univariate and multivariate analysis. Subanalysis was performed for the periods before and after first healthcare contact, which were defined as parental and physician delay, respectively. RESULTS: Mean total delay was 11.6 weeks. Mean parental and physician delay was 7.7 and 4.0 weeks, respectively. Shorter delay was associated with younger age (P<0.05), incidental detection by healthcare workers (P<0.001), and first presentations to pediatricians and nonpediatric emergency departments (P=0.01). Tumor type (P<0.01) and site (P=0.001) were also significantly related. After adjustment for other factors, extracranial germ cell tumors, abdominal tumors and first presentation to nonpediatric emergency departments were significantly associated with shorter total delay. Disease stage remained constant over time, with 30% presenting in stage 4. CONCLUSIONS: Patient age, first healthcare contact, tumor type, and site were significantly related to diagnostic delay in pediatric solid tumors. Our findings reflect factors related to delay in an urban island-state with minimized confounding by healthcare access and geographic factors.
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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 | 0.001 |
| 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) | 0.001 | 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".