Laparoscopic gastric surgery in an enhanced recovery programme
Bibliographic record
Abstract
BACKGROUND: Laparoscopy is associated with less pain and organ dysfunction than open surgery. Improved perioperative care (enhanced recovery programmes, fast-track methodology) has also led to reduced morbidity and a shorter hospital stay. The effects of a combination of laparoscopic resection and accelerated recovery have not been examined previously in the context of gastric surgery. METHODS: This was a prospective study of 32 consecutive patients undergoing laparoscopic gastric resection combined with an enhanced recovery protocol (early oral intake, no drains or nasogastric tubes, no epidural analgesia, use of a urinary catheter for less than 24 h and planned discharge 72 h after surgery). Outcomes included length of hospital stay, intraoperative and postoperative complications, readmission rate and 30-day mortality. RESULTS: Operative procedures were elective distal or subtotal gastrectomy (22 patients) and total gastrectomy (10). Median length of hospital stay was 4 (range 2-30) days. There were two major complications: postoperative bleeding requiring reoperation and pulmonary embolism. Two patients required readmission, one for a wound abscess and one for treatment of a urinary tract infection. There were no deaths within 30 days. CONCLUSION: Minimally invasive gastrectomy with enhanced postoperative recovery results in a short hospital stay and low morbidity rate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".