Comprehensive analysis of in-hospital delirium after major surgical oncology procedures
Bibliographic record
Abstract
INTRODUCTION: Very few population-based assessments of delirium have been performed to date. These have not assessed the implications of delirium after major surgical oncology procedures (MSOPs). We examined the temporal trends of delirium following 10 MSOPs, as well as patient and hospital delirium risk factors. Finally, we examined the effect of delirium on length of stay, inhospital mortality, and hospital charges. METHODS: We retrospectively identified patients who underwent prostatectomy, colectomy, cystectomy, mastectomy, gastrectomy, hysterectomy, nephrectomy, oophorectomy, lung resection, or pancreatectomy within the Nationwide Inpatient Sample (2003-2013). We yielded a weighted estimate of 3 431 632 patients. Multivariable logistic regression (MLR) analyses identified the determinants of postoperative delirium, as well as the effect of delirium on length of stay, in-hospital mortality, and hospital charges. RESULTS: Between 2003 and 2013, annual delirium rate increased from 0.7 to 1.2% (+6.0%; p<0.001). Delirium rates were highest after cystectomy (predicted probability [PP] 3.1%) and pancreatectomy (PP 2.6%), and lowest after prostatectomy (PP 0.15%) and mastectomy (PP 0.13%). Advanced age (odds ratio [OR] 3.80), maleness (OR 1.38), and higher Charlson comorbidity index (OR 1.20), as well as postoperative complications represent risk factors for delirium after MSOPs. Delirium after MSOP was associated with prolonged length of stay (OR 3.00), higher mortality (OR 1.15), and increased in-hospital charges (OR 1.13). CONCLUSIONS: No contemporary population-based assessments of delirium after MSOP have been reported. According to our findings, delirium after MSOP has a profound impact on patient outcomes that ranges from prolonged length of stay to higher mortality and increased in-hospital charges.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".