Evaluation of the efficacy of a novel electronic pain assessment device, the Pain Gauge <sup>®</sup> , for measuring postoperative pain in rats
Bibliographic record
Abstract
One of the major challenges for individuals working with laboratory animals is the recognition and alleviation of pain. The Pain Gauge is marketed as a pain assessment device that measures electrodermal activity. To establish whether the Pain Gauge is effective in assessing postoperative pain in laboratory rats, preoperative and postoperative pain gauge scores ('pain scores') were obtained from 67 rats. Rats were randomly assigned to one of three experimental groups (laparotomy, craniotomy or control) and to one of four analgesic groups (meloxicam [2 mg/kg s.c.] or parecoxib [1, 5 or 20 mg/kg i.v.]). Five consecutive 'pain scores' were obtained from each animal at each of five time points (preprocedure, and at 1, 2, 3 and 4 h postoperatively). Overall there was a significant difference between 'pain scores' at different time points; mainly a decrease at 1 h postoperatively compared with the preoperative scores. There was no overall increase in postoperative 'pain scores' in the rats that were most likely to suffer from postoperative pain (rats given a lower dose of analgesic that underwent a surgical procedure) compared with rats that did not undergo a potentially painful procedure (rats in anaesthesia-only/control group). Therefore it was concluded that the Pain Gauge is ineffective in assessing postoperative pain in rats in this study.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".