Comparison of various predictive tools in predicting risk of cerebrospinal fluid diversion post-resection of posterior fossa tumors: A systematic review
Bibliographic record
Abstract
Background & Objective: Posterior fossa tumors (PFTs) frequently cause hydrocephalus (HCP), requiring permanent cerebrospinal fluid (CSF) diversion post resection in both pediatric and adult patients. We aimed to compare the performance of various tools in predicting the risk of postoperative hydrocephalus and improving prediction for better neurosurgical decision-making. Methodology: A comprehensive literature search was conducted across Google Scholar and PubMed database adhering to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines using keywords such as posterior fossa tumors, hydrocephalus, CSF diversion, and predictive models. A total of ten original articles with a sample size of 1597 from 2021 to 2025 were selected for data extraction. Study quality evaluation was executed via PROBAST tool. Results: The cumulative mean ages were 7.18±1.64 years for pediatric patients and 53.07±0.81 years for adults. Pediatric patients accounted for 67.31% (1075) patients while adults accounted for 19.41% (402) patients. Preoperative hydrocephalus was present in 52.4% (833) patients, out of which 22.9% (367) required CSF diversion post resection. Pooled post-operative shunt rates revealed higher shunt rate 53.9% (715) in patients with preoperative hydrocephalus than those without 13% (80). Logistic regression was used in 88.8% (8) of the identified models while AI based model demonstrated best performance (AUC = 0.938). Conclusion: Of all the predictive models developed, till now, to predict the need for CSF diversion after PFT resection, artificial intelligence-based model shows superior accuracy for improving hydrocephalus risk prognostication. Apart from clinical, demographic, surgery-related and radiological predictors incorporated in conventional predictive models, the artificial-intelligence based model improves risk prediction by utilizing complex patterns in intraoperative and postoperative imaging of patients.
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 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".