[OBJECTIVE ASSESSMENT OF POSTOPERATIVE PAIN AFTER DIGESTIVE TRACT SURGERY].
Bibliographic record
Abstract
Pain is a sensation associated with subjective factors, making it difficult to measure and assess. Currently, there is no widely accepted method of objectively assessing pain, and therefore subjective assessments such as the Visual Analogue Scale (VAS) are generally used. The PainVision system has been developed for the quantitative analysis of pain and comparison of postoperative pain intensity. In this study, we investigated whether postoperative pain could be objectively assessed using this system in digestive tract surgery patients. Pain scores were measured with the VAS, the PainVision system, and the short-form McGill Pain Questionnaire in patients undergoing open or laparoscopic hepatectomy, open or laparoscopic gastrectomy, and laparoscopic cholecystectomy. As measured using the PainVision system, postoperative pain intensity was lower in patients who underwent laparoscopic surgery compared with open hepatectomy. In open hepatectomy patients, pain intensity measured by the PainVision system was significantly lower on postoperative days (POD) 7 and 10 than on POD 1. Preemptive use of nonsteroidal antiinflammatory drugs significantly reduced postoperative pain in open hepatectomy patients. The results showed that PainVision effectively quantifies pain intensity after digestive tract surgery. Objective assessment of postoperative pain may lead to earlier mobility and improved quality of life.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".