Pain management in the neonatal piglet during routine management procedures. Part 2:Grading the quality of evidence and the strength of recommendations
Bibliographic record
Abstract
Piglets reared in swine production in the USA undergo painful procedures that include castration, tail docking, teeth clipping, and identification with ear notching or tagging. These procedures are usually performed without pain mitigation. The objective of this project was to develop recommendations for pain mitigation in 1- to 28-day-old piglets undergoing these procedures. The National Pork Board funded project to develop recommendations for pain mitigation in piglets. Recommendation development followed a defined multi-step process that included an evidence summary and estimates of the efficacies of interventions. The results of a systematic review of the interventions were reported in a companion paper. This manuscript describes the recommendation development process and the final recommendations. Recommendations were developed for three interventions (CO2/O2 general anesthesia, non-steroidal anti-inflammatory drugs (NSAIDs), and lidocaine) for use during castration. The ability to make strong recommendations was limited by low-quality evidence and strong certainty about variation in stakeholder values and preferences. The panel strongly recommended against the use of a CO2/O2 general anesthesia mixture, weakly recommended for the use of NSAIDs and weakly recommended against the use of lidocaine for pain mitigation during castration of 1- to 28-day-old piglets.
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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 | 0.042 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".