Impact of Time to Diagnosis on Morbidity and Survival in Children With Malignant Central Nervous System Tumors
Bibliographic record
Abstract
OBJECTIVE: The aim was to determine the impact of time to diagnosis (TTD) on morbidity and mortality and to identify factors associated with overall survival (OS) in pediatric patients with malignant central nervous system (CNS) tumors. METHODS: This is a retrospective review of all malignant CNS tumors presenting to 2 tertiary care pediatric hospitals from 2000 to 2019. Cox proportional hazard model analysis outcomes included TTD and OS as well as morbidity; stratified by tumor category, age, relapse, and presence of metastatic disease. RESULTS: There were 197 children with malignant CNS tumors (mean age 8.7 y, 61% male). Tumors included medulloblastoma (N=58, 29.4%), ependymoma (N=27, 13.7%), high-grade glioma (N=42, 21.3%), germ cell tumors (N=47, 23.9%), and other embryonal tumors (N=23, 11.7%). Median TTD from symptom onset was 62 (interquartile range: 26.5 to 237.5 d) and 28% had metastatic disease. Three-year progression free survival was 55% and 3-year OS was 73.1%. Increased OS was associated with increased TTD (parameter estimate 0.12; confidence interval [CI]: 0.019-7.06; P =0.019), high-grade glioma (hazard ratio [HR]: 2.46; CI [1.03-5.86]; P =0.042), other embryonal tumor (HR: 2.84; CI [1.06-7.56]; P =0.037), relapse (HR: 10.14; CI: 4.52-22.70; P <0.001) and metastatic disease (HR: 3.25; CI: 1.51-6.96; P =0.002). Vision change (HR: 0.58; CI: 0.313-1.06; P =0.078), hearing loss (HR: 0.71; CI: 0.35-1.42; P =0.355), and cognitive impairment (HR: 0.73; CI: 0.45-1.19; P =0.205) were not associated with TTD in this model. CONCLUSIONS: Increased median TTD is associated with higher OS in pediatric patients treated for malignant CNS tumors. Tumor biology and treatment modality are more important factors than TTD for predicting morbidity and long-term outcomes in pediatric patients with CNS tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".