PTH-253 Pre-operative weight loss in patients undergoing colorectal surgery
Bibliographic record
Abstract
Introduction Malnutrition is associated with poorer post-operative outcomes in patients undergoing surgery. Weight loss and nutritional problems are a common occurrence in cancer and IBD (Irritable Bowel Disease). This study examines the effect of weight loss and complications in a lower gastrointestinal (LGI) surgical population. Method A prospective observational study was undertaken in patients admitted to a GI Unit at St Thomas’ Hospital (October to December 2014) for emergency and elective surgery. Data on unintentional weight loss (6 months prior to admission), LOS (length of stay), and complication occurrence was collated. Results Data on 48 patients was collected; presented in Table 1: Conclusion Pre-operative weight loss was associated with increased LOS and complication occurrence in patients undergoing LGI surgery. Early detection and treatment of malnutrition is an essential consideration in the pre-operative optimisation of patients undergoing LGI surgery. Planned elective admissions should have services in place to address nutritional status prior to surgery. Further prospective randomised studies would help elucidate optimal pre-operative nutritional interventions. Disclosure of interest None Declared. Reference Dindo D, Demartines N, Clavien PA. The Clavien-Dindo Classification of Surgical Complications. Ann Surg. 2004;244:931–937
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 | 0.000 | 0.002 |
| 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".