P23 MALNUTRITION INDICES AS PROGNOSTIC FACTORS FOR POSTOPERATIVE COMPLICATIONS IN ESOPHAGEAL CANCER PATIENTS
Bibliographic record
Abstract
Abstract Aim To investigate the impact of malnutrition on postoperative complications in esophageal cancer patients. Background and Methods Malnutrition is common in esophageal cancer patients due to the debilitating nature of their disease. Several methods of nutritional assessment have emerged as significant prognostic factors for short-and long-term outcomes in patients operated for esophageal cancer. The study sample consisted of 85 patients with esophageal (n=11) and gastroesophageal junction (n=74) cancer who were admitted for surgery in the First Department of Surgery, Laikon General Hospital, Athens, Greece, between September 2015 and March 2019. Out of them, 65 patients underwent esophagectomy, while 20 patients underwent total gastrectomy. The assessment of nutritional status included the Geriatric Nutritional Risk Index (GNRI), the Patient Generated Subjective Global Assessment (PG-SGA) and sarcopenia. GNRI was based on preoperative values of patients’ serum albumin and body weight. The preoperative assessment of sarcopenia was based on Skeletal Muscle Index (SMI) derived from analysis of CT scans using SliceOmatic® Software version 4.3 (Tomovision, Montreal, Canada). Postoperative complications were graded according to Clavien-Dindo classification. Minor complications included categories I-II, whereas major complications included categories III-V. Results Thirty nine patients (47.6%) developed postoperative complications. More specifically, 21 patients (24.7%) developed minor complications and 18 patients (21.2%) developed major complications, while anastomotic leakage occurred in 10 patients (11.8%). Eighty patients (94.1%) had a high-risk GNRI (<92), while 5 patients (5.9%) had a low-risk GNRI (≥92). Forty four patients (51.8%) were diagnosed with sarcopenia. The mean PG-SGA score was 8.82 ± 5.57. Patients with a high-risk GNRI demonstrated significantly higher rate of overall complications compared to low-risk GNRI patients (100% vs 44.2%, p<0.05 respectively). Moreover, the rate of anastomotic leakage was significantly higher in the sarcopenia group than in the non-sarcopenia group (29% vs 3.4%, p<0.05). Nonetheless, PG-SGA was not significantly associated with postoperative outcomes. Conclusion Higher-risk scores on the GNRI are associated with an increased risk for developing postoperative complications, while sarcopenia is associated with higher risk for anastomotic leakage among esophageal cancer patients. Preoperative assessment of GNRI and sarcopenia should be performed in all patients in order to detect patients who are at greater risk of postoperative morbidity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".