ASSOCIATION BETWEEN FRAILTY AND POSTOPERATIVE COMPLICATIONS IN PATIENTS UNDERGOING ABDOMINAL SURGERY
Bibliographic record
Abstract
Background: Although several studies relate frailty with poor postoperative outcomes, trials with specific intraabdominal surgery cohorts are scarce. Objetives: To evaluate the association between frailty and 1 month postoperative complications, and 3 months mortality Materials and Methods: Observational, descriptive and analytical study of a prospective cohort. Patients older than 70 years who underwent elective surgery were evaluated in the preoperative area of Italian Hospital from Buenos Aires, with the Edmonton Frail Scale. Data were collected between June 4, 2014 and February 1, 2017. We estimated mortality risk and complication risk with a logistic regression. We reported OR and 95% confidence intervals. A multivariate logistic regression analysis was performed to control confounding Results: We included 85 patients, 18% (15) were frail, mean age 80.3 years old (SD 7.1). The non frail group was younger 76.4 (SD 5.5). Overall 3 months mortality was 20% (3) for frail and 1.4% (1) for non frail patients, OR 17.2 (IC95% 1.65–179.9, p 0.02). After adjusting for sex, age, comorbidity and oncologic surgery this association persisted statistically significant, OR 36.8 (IC95% 2.4–543.9, p 0.01). 53.3% (8) of frail patients and 17.1% (12) of non frail patients had complications within 1 month postoperatively, OR 5.5 (IC95 % 1.6–18, p 0.01) and after adjusting for confounders this association persisted statistically significant OR 5.71 (1.43–22.7, p0.01). Conclusion: In this population, the presence of frailty was associated with a significant increase in overall posoperative complications and death.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".