Subtotal tumor resection as a predictor of post-resection hydrocephalus in pediatric patients with posterior fossa tumors
Bibliographic record
Abstract
OBJECTIVE: Pediatric posterior fossa tumor (PFT) resection is frequently complicated by postoperative hydrocephalus. Previous hydrocephalus risk stratification tools demonstrate limited performance in external validation and do not account for important intra- and post-operative variables. We aimed to evaluate additional risk factors for post-resection hydrocephalus to improve risk-stratification. METHODS: We conducted a retrospective analysis of pediatric patients who underwent resection of primary PFT's at our institution (January 2016-June 2024). We collected perioperative variables thought to influence hydrocephalus risk. The primary outcome was permanent CSF diversion within 6 months after resection. We used univariable and multivariable logistic regression with bidirectional stepwise selection to identify predictors of post-resection hydrocephalus. RESULTS: residual tumor) as independent predictors of post-resection hydrocephalus on multivariable regression. STR exhibited an odds ratio of 8.25 (95% CI 2.72-26.84; p < 0.001), highlighting its strong association with the need for permanent CSF diversion. A scoring tool that incorporated STR and an age cutoff of < 5 years improved the discriminative performance (area under the ROC curve = 0.826) compared to the modified Canadian Preoperative Prediction Rule for Hydrocephalus (AUC = 0.720). CONCLUSIONS: These findings suggest STR is associated with increased risk of persistent hydrocephalus and emphasize careful intraoperative decision-making when pursuing incomplete resection. Future multicenter studies will be necessary to validate this framework and to further elucidate how STR, in conjunction with age and hydrocephalus severity, can be leveraged to optimize treatment strategies in diverse healthcare settings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".