Predicting postresection hydrocephalus in pediatric patients with posterior fossa tumors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECT: Approximately 30% of children with posterior fossa tumors exhibit hydrocephalus after tumor resection. Recent literature has suggested that prophylactic endoscopic third ventriculostomy diminishes the risk of this event. Because the majority of patients will not have postoperative hydrocephalus, a preoperative clinical prediction rule that identifies patients at high or low risk for postresection hydrocephalus would be helpful to optimize the care of these children. METHODS: The authors evaluated a derivation cohort of 343 consecutive children with posterior fossa tumors who underwent treatment between 1989 and 2003. Multivariate methods were used on these data to generate the Canadian Preoperative Prediction Rule for Hydrocephalus. The rule's estimated risk of postresection hydrocephalus was compared with risk observed in 111 independent patients in the validation cohort. RESULTS: Variables identified as significant in predicting postresection hydrocephalus were age < 2 years (score of 3), papilledema (score of 1), moderate to severe hydrocephalus (score of 2), cerebral metastases (score of 3), and specific estimated tumor pathologies (score of 1). Patients with scores > or = 5 were deemed as high risk. Predicted probabilities for the high- and low-risk groups were 0.73 and 0.25, respectively, from the derivation cohort, and 0.59 and 0.14 after prevalence adjustment compared with the observed values of 0.42 and 0.17 in the validation cohort. CONCLUSIONS: A patient's score on the Preoperative Prediction Rule for Hydrocephalus will allow improved patient counseling and surgical planning by identifying patients at high risk of developing postresection hydrocephalus. These patients might selectively be exposed to the risks of preresection CSF diversion to improve outcome.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it