Frailty predicts mortality in patients with upper gastrointestinal bleed: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND AND AIM: Evidence on the impact of frailty in patients with upper gastrointestinal bleed (UGIB) is limited. This study aims to define the role of frailty as defined by Canadian Study of Health and Aging clinical frailty scale (CSHA-CFS) in predicting mortality in UGIB. METHODS: A prospective single-center cohort study was conducted over 21 months on all consecutive patients with UGIB. Data on demographics, lab parameters, Glasgow Blatchford score, CSHA-CFS, Charlson Comorbidity Index, and AIMS65 score was recorded. The primary outcome was all-cause inpatient mortality. The secondary outcomes were all-cause 30-day mortality, 30-day rebleeding, 30-day readmission, hospital length of stay (LoS), intensive care unit (ICU) admission, need for repeat endoscopy, and need for blood transfusion. The data were evaluated using univariate and multivariate analysis. RESULTS: There were 298 eligible patients, of which 63% were males, median age was 68 years, 44% were from non-English-speaking background, and 72% had major comorbidities. The all-cause inpatient and 30-day mortality were 9.4% and 10.7%, respectively. In the multivariate analysis, CHSA-CFS was the independent predictor of all-cause inpatient mortality (OR 1.66; 95% CI 1.13-2.143; P = 0.010) and all-cause 30-day mortality (OR 1.83; 95% CI 1.26-2.67; P = 0.002). CHSA-CFS was not a significant predictor of 30-day rebleed, 30-day readmission, ICU admission, hospital LoS, or need for blood transfusion. CONCLUSION: Frailty is an important independent predictor of mortality in patients with UGIB. Frailty assessment can guide clinical decision making and allow targeting of health-care resource (Australia/New Zealand Clinical Trial Registry number: ACTRN12622000821796).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".