The initial treatment of hydrocephalus: An assessment of surgeons' preference between third ventriculostomy and shunt insertion
Bibliographic record
Abstract
Third ventriculostomy is an option for patients who have traditionally received a ventriculoperitoneal shunt. This study has been conducted to determine: 1. How common is third ventriculostomy as the initial treatment of hydrocephalus? 2. Does the frequency of third ventriculostomy vary among surgeons? 3. What factors influence surgeons' decision to choose third ventriculostomy? Surgeons completed a questionnaire addressing patient selection and technique factors. Nine case scenarios were reviewed by surgeons who were then asked to choose a ventriculoperitoneal shunt or a third ventriculostomy as the initial treatment. Forty-three responses were received. The proportion of new patients treated with third ventriculostomy varied widely (0%-100%, median 13%). This was not related to years in practice, type of training or presence of residents/fellows. Factors that increased the chance of a third ventriculostomy were triventricular hydrocephalus on CT/MR, isolated aqueduct stenosis, thin ballooned floor and tectal tumor. Factors that decreased the chance of a third ventriculostomy were dilated subarachnoid spaces, meningitis and head injury. The presence of myelomeningocele or age < 1 year were less likely to influence the choice of operation. Variation in the rate of third ventriculostomy as the first treatment for hydrocephalus is large. It is unlikely that this degree of variation can be explained by differences in patient populations. Further work to refine and disseminate the indications for third ventriculostomy is warranted.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".