Surgeons’ aims and pain assessment strategies when managing paediatric post-operative pain
Bibliographic record
Abstract
Children experience moderate to severe pain post-operatively. Nurses have been found to have a variety of aims in this context. Surgeons' aims when managing post-operative pain have not been explored. This qualitative study set out to explore paediatric surgeons' aims when managing post-operative pain in one paediatric hospital in Canada. Consultant surgeons (n = 8) across various specialities took part in semi-structured interviews. Surgeons' overarching aim was to keep the child comfortable. Various definitions of comfortable were given, relating to the child's experience of pain itself and their ability to undertake activities of daily living. Children's behavioural pain cues seem to be a primary consideration when making treatment decisions. Parents' views regarding their child's pain were also seen as important, suggesting children may not be seen as competent to make decisions on their own behalf. The need to maintain a realistic approach was emphasised and pain management described as a balancing act. Surgeons may draw on both tacit and explicit knowledge when assessing children's pain. There appears to be an expectation among surgeons that some pain is to be expected post-operatively and that the diagnostic value of pain may, in some cases, supersede concerns for the child's pain experience.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".