Predictors of Postoperative Complications in Vestibular Schwannoma Surgery—A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: To investigate preoperative patient demographics and comorbidities in relation with postsurgical complications following vestibular schwannoma surgery. STUDY DESIGN: Retrospective population-based cohort study. SETTING: All hospitals in the Canadian province of Ontario. PATIENTS: This study includes 1,456 patients who underwent vestibular schwannoma surgery from April 1, 2002 to March 31, 2018 in Ontario, Canada. INTERVENTION/OUTCOME MEASURES: For all surgical patients, the demographic data, preoperative comorbidities, and postoperative complications were evaluated. Postoperative complications were examined immediately following surgery in the hospital as well as 1 year following the hospital discharge. RESULTS: The most common comorbidities in this cohort were hypertension (30.22%), diabetes (9.48%), asthma (13.53%), and chronic obstructive pulmonary disease (6.73%). Diabetes was the most impactful comorbidity and was associated with higher risk of myocardial infarction (RR = 4.58, p < 0.01), pneumonia (RR = 1.80, p = 0.02), dysphagia (RR = 1.58, p < 0.01), and meningitis (RR = 3.62, p < 0.01). Analysis of surgical approaches revealed that the translabyrinthine approach, compared with the open craniotomy approach, was negatively associated with postoperative complications including pneumonia (RR = 0.43, p < 0.01), urinary tract infection (RR = 0.55, p = 0.01), dysphagia (RR = 0.66, p < 0.01), and readmission (RR = 0.45, p < 0.01). CONCLUSION: This study examines patient demographics, preoperative comorbidities, and postoperative complications in patients who have undergone vestibular schwannoma surgery. The results highlight associations between patient characteristics and postoperative outcomes that can aid in preoperative decision-making and counselling.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".