Prognostic factors for morbidity and mortality in elderly patients undergoing acute gastrointestinal surgery: a systematic review
Bibliographic record
Abstract
BACKGROUND: Elderly patients undergoing acute gastrointestinal (GI) surgery experience increased morbidity and mortality compared with younger and elective patients. Prognostic factors can be used to counsel patients of these risks and, if modifiable, to minimize them. We reviewed the literature on prognostic factors for adverse outcomes in elderly patients undergoing acute GI surgery. METHODS: We searched PubMed and Embase using a strategy developed in collaboration with an expert librarian. Studies examining independent associations between prognostic factors and morbidity or mortality in patients aged 65 and older undergoing acute GI surgery were selected. We extracted data using a standardized form and assessed study quality using the QUIPS tool. RESULTS: Nine cohort studies representing 2958 patients satisfied our selection criteria. All studies focused on postoperative mortality. Thirty-four prognostic factors were examined, with significant variability across studies. There was limited or conflicting evidence for most prognostic factors. Meta-analysis was only possible for the American Society of Anesthesiologists (ASA) score, which was found to be associated with mortality in 4 studies (pooled odds ratio 2.77, 95% confidence interval 0.92-8.41). CONCLUSION: While acute GI surgery in elderly patients is becoming increasingly common, the literature on prognostic factors for morbidity and mortality in this patient population lags behind. Further research is needed to help guide patient care and potentially improve outcomes.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.002 | 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".