Analysis of factors that influence neurosurgical length of hospital stay among newly diagnosed pediatric brain tumor patients
Bibliographic record
Abstract
BACKGROUND: Postoperative length of stay (LOS) carries a high burden of healthcare costs. In resource-intense specialties such as neurosurgery, it is imperative to identify factors that influence LOS to improve care. The current study investigates the potential for variables that affect clinical presentation, tumor characteristics, treatment modalities, and postoperative complications to impact overall LOS in pediatric brain tumor patients. METHODS: A retrospective cohort study design was used with patients enrolled in the McMaster Pediatric Brain Tumor Study Group database. All patients up to 18 years of age, presenting with a newly diagnosed brain tumor admitted to and discharged from neurosurgery, were included. Patients were sorted into three cohorts: short LOS (≤3 days), extended LOS (≥20 days), and control LOS (4-19 days). RESULTS: Of the 124 patients included, 20 (65% male; median age: 9.1 years; range, 0.8-17.4 years) were considered short LOS, 28 (61% male; median age: 4.7 years; range, 0.4-14.7 years) were considered extended LOS, and 76 (57% male; median age: 8.5 years; range, 0.3-17.9 years) were considered control LOS. Variables that prolonged LOS were emesis at presentation (P < 0.001), developmental delay (P = 0.02), multiple surgeries (P = 0.004), tumor location (P < 0.05), subtotal resection (P = 0.02), feeding tube (P < 0.001), adjuvant chemoradiotherapy (P < 0.001), and posterior fossa syndrome (P = 0.004). CONCLUSIONS: This study identifies variables related to clinical presentation, tumor characteristics, treatment modalities, and postoperative complications associated with extended LOS. These findings uncover novel predictors of LOS that can be used to guide future research and improve health resource management.
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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 | 0.001 | 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".