A Novel Method for Evaluating Postoperative Adhesions in Rats
Bibliographic record
Abstract
Purpose/Aim: Postoperative adhesions remain an undesirable and commonly symptomatic side effect of abdominopelvic surgeries. Animal models of postoperative adhesions typically yield heterogeneous adhesions throughout the abdominal cavity and are not easily quantified. Here we present a novel method of postoperative adhesion assessment and report its reliability and measurement error. MATERIALS AND METHODS: A model of cecal abrasion with partial sidewall attachment was performed on female rats. After 1, 2, 4, or 7 days of recovery, the rats were euthanized and their abdominopelvic cavities were systematically evaluated for postoperative adhesions. The necropsy was recorded through the surgical microscope. Four raters were trained to use a ballot to capture key factors of the adhesions as they viewed the recordings. Their ratings were compared for measurement error and reliability (using Bland-Altman plots and intraclass correlation coefficients, respectively) and for the ability to discriminate differences in experimental groups. A subset of the data was analyzed to determine practical utility. RESULTS: The rating system was shown to have low measurement error and high inter-rater reliability for all parameters measured. Applied practically, the system was able to discriminate groups in a manner that was expected. CONCLUSIONS: We have developed and validated a rating system for postoperative adhesions and shown that it can detect group differences. This method can be used to quantify postoperative adhesions in rodent models.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".