Use of the ETV Success Score to explain the variation in reported endoscopic third ventriculostomy success rates among published case series of childhood hydrocephalus
Bibliographic record
Abstract
OBJECT: Published case series of endoscopic third ventriculostomy (ETV) for childhood hydrocephalus have reported widely varying success rates. The authors recently developed and internally validated the ETV Success Score (ETVSS); this is a simplified means of predicting the 6-month success rate of ETV for a child with hydrocephalus, based on age, etiology of hydrocephalus, and presence of a previous shunt. The authors hypothesized that the ETVSS would be able to predict with reasonable accuracy the actual ETV success rate reported among published case series. METHODS: A literature search was performed to identify published pediatric ETV papers that contained enough information with which to calculate an aggregate, mean predicted ETVSS for the cohort. This was then compared with the actual ETV success rate in the cohort. Data were extracted independently in triplicate, including by 2 individuals who were not involved with the development of the ETVSS. RESULTS: Fifteen papers reporting on 322 patients were included. Interrater reliability was very high in determining the predicted ETVSS (intraclass correlation coefficient 0.99). The predicted ETVSS for each paper agreed strongly with the actual ETV success rate reported in each paper (reliability intraclass correlation coefficient 0.81). There was no significant difference in the magnitude of the predicted ETVSS and the actual ETV success (p = 0.98, paired t-test). In a linear regression model, the predicted ETVSS explained 62% of the variation in actual ETV success. When the entire cohort was combined and analyzed together, the overall mean predicted ETVSS was 57.9%, which was nearly identical to the actual ETV success rate of 59.2%. CONCLUSIONS: The ETVSS closely predicts the actual ETV success rate reported in selected papers published over the last 20 years and explains much of the variation.
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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 | 0.058 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.043 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".