A qualitative assessment of perceptions and attitudes toward postoperative pain and opioid use in patients undergoing elective knee arthroscopy
Bibliographic record
Abstract
BACKGROUND: Orthopedic surgeons routinely prescribe opioids to manage post-operative pain. In the face of an opioid epidemic, a one-size-fits-all approach to pain management is no longer appropriate. Patient-centred prescribing practices should be used by surgeons; however, little is known about what influences patient attitudes toward postoperative pain and its management to inform such practices. We sought to explore patient attitudes toward postsurgical pain management, including opioids. METHODS: We conducted qualitative, semistructured interviews of 11 opioid-naive patients (age 16-46 yr) who were scheduled to undergo arthroscopic knee surgery. Transcripts were analyzed thematically using a framework analysis that involved familiarization, developing a thematic framework, indexing, charting and mapping, and interpretation. RESULTS: Participant attitudes toward postoperative pain and opioids were influenced by perceived tolerance to pain based on personal experience, perceived predisposition to addiction based on personal assumptions regarding addictive personality traits and risk factors, and perceptions of opioid use shaped by external influences, including family, friends and the media's depiction of the opioid epidemic. Every patient expressed that preoperative counselling and education regarding postoperative pain management would be beneficial in improving their knowledge base, easing anxieties and clarifying misunderstandings. CONCLUSION: Surgeons can address the patient-reported factors identified in this study to help optimize a patient's perioperative experience without relying solely on prescribed analgesia. By improving accessibility to education and promoting safe, patient-centred prescribing practices, we may reduce reliance on opioids in orthopedic surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".