403. FRAILTY AS A PREDICTOR OF POSTOPERATIVE COMPLICATIONS IN PATIENTS UNDERGOING ESOPHAGECTOMY
Bibliographic record
Abstract
Abstract Careful selection and optimization of esophageal cancer patients for esophagectomy is necessary and has been shown to improve outcomes. Preoperative frailty measures, both objective and subjective, may predict postoperative outcomes after minimally invasive esophagectomy (MIE) and be applied to surgical decision-making and patient selection. We hypothesize that measures of increased frailty are associated with major complications after MIE for esophageal cancer. This is a prospective cohort study of patients undergoing MIE for esophageal cancer in a US academic tertiary hospital over one year (2021). We tested preoperative frailty by measuring two-flight stair climb times, pulse change during stair climb, grip strength, and Edmonton Frail Scale (EFS) score. Postoperative complications in the first 30 days were classified based on Clavien-Dindo, with Grade III or above defined as a major complication (primary outcome). We calculated Spearman correlations among the four different frailty measures. We performed t-tests to compare frailty measures in patients with and without major complications. Thirty-four patients were enrolled with a mean age of 64 (SD 11) and 79.4% male. EFS score, grip strength, and stair climb time were significantly correlated (p < 0.05), as shown in Figure 1. There was a significant difference in EFS scores between groups with or without major complications (4.7 vs 3.0, p = 0.04, respectively), with a higher score representing increased frailty. Other frailty measures did not meet significance between groups for major complication, including stair climb time (difference of 11.0 s, 95% CI: −0.38-22.4) and grip strength (difference of 7.7 kg, 95% CI: −0.94-16.3). No difference was seen in pulse change by complication status. A higher Edmonton Frail Scale score is associated with major postoperative complications following MIE. Patients with higher EFS score should be counseled on increased surgical risk of morbidity and prehabilitation should be considered. Other measures such as stair climb time and grip strength may support the preoperative frailty assessment in esophageal cancer patients, as they were highly correlative with each other.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".