P.075 Rates of infection following craniotomy or craniectomy with subsequent cranioplasty in traumatic brain injury
Bibliographic record
Abstract
Background: Postoperative infection is a significant cause of morbidity and mortality in traumatic brain injury (TBI) patients who undergo craniotomy and/or craniectomy. Data on the rates of infections associated with these procedures are limited. We present a single-center retrospective study on the rates of infection in post-traumatic craniotomies, craniectomies and cranioplasties. Methods: Data on 100 TBI adult patients who underwent a craniotomy, craniectomy and/or cranioplasty from 2011-2015 will be analyzed. Demographic and perioperative data including open/closed TBI, peri/postoperative infections, duration of procedure, type and mode of bone flap preservation will be retrieved. Results: Following our data collection (to be completed by the end of February), we expect infection rates of 3-20% in our study. Upon instituting a protocol similar to the Hydrocephalus Clinical Research Network’s (HCRN) ventriculoperitoneal shunt (VP) protocol, we hope to reduce our post-TBI craniotomy/craniectomy/cranioplasty infections rates to less than 10%. Our projection is based on the HCRN protocol’s 3.15% absolute risk reduction of VP shunt infections. Conclusions: The results of this study will emphasize the need for instituting robust perioperative protocols to reduce infections. Further research will be pursued following this study to establish a protocol similar to the VP shunt protocol from the HCRN, in an attempt to reduce perioperative rates of infection.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".