Validation and modification of a predictive model of postresection hydrocephalus in pediatric patients with posterior fossa tumors
Bibliographic record
Abstract
OBJECT: Postresection hydrocephalus is observed in approximately 30% of pediatric patients with posterior fossa tumors. However, which patients will develop postresection hydrocephalus is not known. The Canadian Preoperative Prediction Rule for Hydrocephalus (CPPRH) was developed in an attempt to identify this subset of patients, allowing for the optimization of their care. The authors sought to validate and critically appraise the CPPRH. METHODS: The authors conducted a retrospective chart review of 99 consecutive pediatric patients who presented between 2002 and 2010 with posterior fossa tumors and who subsequently underwent resection. The data were then analyzed using bivariate and multivariate analyses, and a modified CPPRH (mCPPRH) was applied. RESULTS: Seventy-six patients were evaluated. Four variables were found to be significant in predicting postresection hydrocephalus: age younger than 2 years, moderate/severe hydrocephalus, preoperative tumor diagnosis, and transependymal edema. The mCPPRH produced observed likelihood ratios of 0.737 (95% CI 0.526-1.032) and 4.688 (95% CI 1.421-15.463) for low- and high-risk groups, respectively. CONCLUSIONS: The mCPPRH utilizes readily obtainable and reliable preoperative variables that together stratify children with posterior fossa tumors into high- and low-risk categories for the development of postresection hydrocephalus. This new predictive model will aid patient counseling and tailor the intensity of postoperative clinical and radiographic monitoring for hydrocephalus, as well as provide evidence-based guidance for the use of prophylactic CSF diversion.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".