Risk Factors for Postoperative Delirium in Patients Undergoing Major Head and Neck Cancer Surgery
Bibliographic record
Abstract
BACKGROUND: Postoperative delirium (POD) is a common and serious complication after extensive surgery. Understanding the independent and potential modifiable risk factors leading to POD in patients with head and neck cancer (HNC) can provide information for future intervention trials aimed at reducing this risk. OBJECTIVE: To systematically analyze influencing factors of POD in patients with HNC and identify high-risk individuals for delirium. METHODS: PubMed, EMBASE, Scopus, OVID, and Cochrane Library were searched for publications prior to June 2023. Comparative studies in which POD risk factors were investigated were identified following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The Newcastle-Ottawa Scale was used to evaluate the study quality. Pooled odds ratios or mean differences for individual risk factors were estimated using the Mantel-Haenszel and inverse-variance methods. RESULTS: This review included 17 studies with a total of 4188 patients undergoing HNC surgery. The pooled prevalence of POD was 15.44%. Based on pooled analysis, 8 significant risk factors were identified including age older than 70 years, male sex, history of smoking, history of psychiatric disorder, American Society of Anesthesiologists score, albumin level, postoperative insomnia, and fluid intake. CONCLUSION: In the present study, 8 factors that correlated with POD were identified: 6 preoperative, 1 intraoperative, and 1 postoperative. IMPLICATIONS FOR PRACTICE: The influencing factors for POD in patients with HNC were identified that can provide a reference for improving the psychological state of the patient population and further development of effective treatment interventions.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".