Opioid Induced Hyperalgesia, a Research Phenomenon or a Clinical Reality? Results of a Canadian Survey
Bibliographic record
Abstract
BACKGROUND: Very little is known regarding the prevalence of opioid induced hyperalgesia (OIH) in day to day medical practice. The aim of this study was to evaluate the physician's perception of the prevalence of OIH within their practice, and to assess the level of physician's knowledge with respect to the identification and treatment of this problem. METHODS: An electronic questionnaire was distributed to physicians who work in anesthesiology, chronic pain, and/or palliative care in Canada. RESULTS: Of the 462 responses received, most were from male (69%) anesthesiologists (89.6%), in the age range of 36 to 64 years old (79.8%). In this study, the suspected prevalence of OIH using the average number of patients treated per year with opioids was 0.002% per patient per physician practice year for acute pain, and 0.01% per patient per physician practice year for chronic pain. Most physicians (70.2%) did not use clinical tests to help make a diagnosis of OIH. The treatment modalities most frequently used were the addition of an NMDA antagonist, combined with lowering the opioid doses and using opioid rotation. CONCLUSIONS: The perceived prevalence of OIH in clinical practice is a relatively rare phenomenon. Furthermore, more than half of physicians did not use a clinical test to confirm the diagnosis of OIH. The two main treatment modalities used were NMDA antagonists and opioid rotation. The criteria for the diagnosis of OIH still need to be accurately defined.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".