266. NSQIP 5-FACTOR MODIFIED FRAILTY INDEX PREDICTS MORBIDITY BUT NOT MORTALITY AFTER ESOPHAGECTOMY
Bibliographic record
Abstract
Abstract Background Frailty is a source of morbidity in esophagectomy patients. To aid clinical decision making, it is important to determine if the simplified 5-factor modified frailty index (mFI-5) retains the same associations between frailty and adverse outcomes as its 11-factor predecessor. Methods Patients undergoing esophagectomy for esophageal cancer or dysplasia from 2016-2018 were identified using National Surgical Quality Improvement Program (NSQIP). The mFI-5 was used to determine association between frailty and post-esophagectomy morbidity and mortality. Results In total, 2,567 patients were included. No patients had a mFI-5 score higher than 3/5 and the score distribution was wide: mFI0 = 1103 (43%), mFI1 = 982 (38.3%), mFI2 = 435 (16.9%), mFI3 = 47 (1.8%). Clavien-Dindo grade IV complications increased with mFI score: mFI0 (11.2%), mFI1 (15.6%), mFI2 (19.8%), mFI3 (25.5%), as did mortality: mFI0 (1.9%), mFI1 (3%), mFI2 (3.4%), mFI3 (4.3%). Multivariate logistic regression analyses controlling for age, sex, body mass index, American Society of Anesthesiology classification, operative duration, emergency surgery status, and neoadjuvant therapy status showed increasing frailty score was associated with Clavien-Dindo grade IV complications (p=0.01), but not mortality (p=0.13). Conclusions The mFI-5 is associated with morbidity but not mortality in patients who have undergone esophagectomy for esophageal cancer. Compared to the mFI-11, the mFI-5 may not be nuanced enough in the assessment of frailty specific to this patient population.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".