The Preventable Shunt Revision Rate: A Multicenter Evaluation
Bibliographic record
Abstract
BACKGROUND: The Preventable Shunt Revision Rate (PSRR) was recently introduced as a novel quality metric. OBJECTIVE: To evaluate the PSRR across multiple centers and determine associated variables. METHODS: Nine participating centers in North America provided at least 2 years of consecutive shunt operations. Index surgery was defined as new shunt implantation, or revision of an existing shunt. For any index surgery that resulted in a reoperation within 90-days, index surgery information (demographic, clinical, and procedural) was collected and a decision made whether the failure was potentially preventable. The 90-day shunt failure rate and PSRR were calculated per institution and combined. Bivariate analyses were performed to evaluate individual effects of each independent variable on preventable shunt failure followed by a final multivariable model using a backward model selection approach. RESULTS: A total of 5092 shunt operations were performed; 861 failed within 90 days of index operation, resulting in a 16.9% combined 90-day shunt failure rate and 17.6% median failure rate (range, 8.7%-26.9%). Of the failures, 307 were potentially preventable (overall and median 90-day PSRR, 35.7% and 33.9%, respectively; range, 16.1%-55.4%). The most common etiologies of avoidable failure were infection (n = 134, 44%) and proximal catheter malposition (n = 83, 27%). Independent predictors of preventable failure (P < .05) were lack of endoscopy (odds ratio [OR] = 2.26), recent shunt infection (OR = 3.65), shunt type (OR = 2.06) and center. CONCLUSION: PSRR is variable across institutions, but can be 50% or higher. While the PSRR may never reach zero, this study demonstrates that overall about a third of early failures are potentially preventable.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".