Experience and Management of the Adverse Effects of Analgesics After Surgery: A Pediatric Patient Perspective
Bibliographic record
Abstract
After surgery, the adverse effects (AEs) of analgesics are common and critical factors influencing the postoperative experience of pediatric patients. Inadequate management of AEs has been found to prolong hospital stay, increase readmission rates and decrease satisfaction with care. The aim of this qualitative descriptive study was to better understand the AEs of analgesics from the perspective of adolescent patients with idiopathic scoliosis after spinal surgery. A total of 7 patients participated in the study. Semistructured interviews were conducted at discharge and 1 week after discharge. Transcribed data were analyzed using qualitative content analysis and themes were identified. Overall, participants most frequently reported gastrointestinal and cognitive AEs, with constipation being the most persistent and bothersome. The pediatric participants used a combination of 3 strategies to mitigate analgesic AEs, namely pharmacologic, nonpharmacologic, and reduction of analgesic intake. Participants demonstrated a lack of understanding of AEs and involvement in their own care. Future studies should be conducted to evaluate the efficacy of nonpharmacological strategies in managing analgesic AEs for pediatric patients after surgery.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".