Temporal trends in surgical procedures for pediatric hydrocephalus: an analysis of the Hydrocephalus Clinical Research Network Core Data Project
Bibliographic record
Abstract
OBJECTIVE: Analysis of temporal trends in patient populations and procedure types may provide important information regarding the evolution of hydrocephalus treatment. The purpose of this study was to use the Hydrocephalus Clinical Research Network's Core Data Project to identify meaningful trends in patient characteristics and the surgical management of pediatric hydrocephalus over a 9-year period. METHODS: The Core Data Project prospectively collected patient and procedural data on the study cohort from 9 centers between 2008 and 2016. Logistic and Poisson regression were used to test for significant temporal trends in patient characteristics and new and revision hydrocephalus procedures. RESULTS: The authors analyzed 10,149 procedures in 5541 patients. New procedures for hydrocephalus (shunt or endoscopic third ventriculostomy [ETV]) decreased by 1.5%/year (95% CI -3.1%, +0.1%). During the study period, new shunt insertions decreased by 6.5%/year (95% CI -8.3%, -4.6%), whereas new ETV procedures increased by 12.5%/year (95% CI 9.3%, 15.7%). Revision procedures for hydrocephalus (shunt or ETV) decreased by 4.2%/year (95% CI -5.2%, -3.1%), driven largely by a decrease of 5.7%/year in shunt revisions (95% CI -6.8%, -4.6%). Concomitant with the observed increase in new ETV procedures was an increase in ETV revisions (13.4%/year, 95% CI 9.6%, 17.2%). Because revisions decreased at a faster rate than new procedures, the Revision Quotient (ratio of revisions to new procedures) for the Network decreased significantly over the study period (p = 0.0363). No temporal change was observed in the age or etiology characteristics of the cohort, although the proportion of patients with one or more complex chronic conditions significantly increased over time (p = 0.0007). CONCLUSIONS: Over a relatively short period, important changes in hydrocephalus care have been observed. A significant temporal decrease in revision procedures amid the backdrop of a more modest change in new procedures appears to be the most notable finding and may be indicative of an improvement in the quality of surgical care for pediatric hydrocephalus. Further studies will be directed at elucidation of the possible drivers of the observed trends.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".