2012 Scientific Session of the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) San Diego, California, USA, 7–10 March 2012 Poster Presentations
Bibliographic record
Abstract
Background: Shoulder pain is a common complaint following laparoscopic surgery and the incidence of Shoulder pain is generally about 35% of patients following laparoscopic surgeries, but it is more frequent after some specific type of laparoscopic surgeries like as Nissen fundoplication and laparoscopic adjustable gastric band.This investigation evaluates the role of slow deflation of abdominal gas in decreasing the severity and the incidence of this problem.Patients and Method: 78 patients with uncomplicated gall stone and early phase of acute cholecystitis were randomly divided in two groups.In control group (38 patients), Ports were removed rapidly and gas deflated in less than 10 s, but in main group (40 patients), gas was deflated with a slow rate in 3 min.Patients were followed for 8 weeks after operation and the incidence and severity of pain were evaluated and compared with each other.Results: The incidence of post laparoscopic shoulder pain was 8% and 34% in main group and control groups, respectively (P \ 0.05).Pain score system shows less severity of pain in investigated group that can lead to decrease in analgesic prescription and dosage.Recommendation: Decreasing the speed of abdominal gas deflation at the end of laparoscopic surgeries, can significantly decrease the incidence and severity of post laparoscopic shoulder pain. 38848P018
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.199 | 0.077 |
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".