Ventriculoperitoneal Shunt 30-Day Failure Rate
Bibliographic record
Abstract
BACKGROUND: With the need for transparency of surgical results, 30-day outcome measures have become increasingly important. Ventriculoperitoneal (VP) shunt failure is a substantial burden to patients and health care systems. OBJECTIVE: This study introduces the 30-day VP shunt failure rate as a possible barometer of surgical outcome and demonstrates its use in a national (United Kingdom [UK]) study and makes comparison with 2 published randomized, controlled trials (RCT). METHODS: A cohort study of all (except 1) pediatric neurosurgical centers in the UK and Ireland. All new and revision VP shunt operations were recorded for 2008 and 2009. Both newly placed and revised VP shunts were subject to Kaplan-Meier analysis, and 30-day failure rate was obtained. Data from 2 RCTs investigating new VP shunt technology were analyzed, and the 30-day failure rate was extracted for comparative purposes. RESULTS: The overall 30-day and 1-year failure rates for new shunts were 12.9% and 28.8%, respectively. The 30-day failure rate from 2 RCTs was comparable (14% and 16%, respectively). The failure rate of the subsequent revision of those new shunts was 20.7% at 30 days and 40.4% at 1 year. According to these data, shunt survival appears to be better if performed by a consultant pediatric neurosurgeon for revision surgery only. CONCLUSION: VP shunt survival in the UK is comparable to the published multicenter data investigating shunt survival. The 30-day failure rate may represent a better barometer of surgical outcome and should be used as a separate outcome measure in the design of future trials.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".