Regional Anesthesia and Pain Survey of chronic pain practice by anesthesiologists
Bibliographic record
Abstract
Results: The overall response rate was 53%. While 38% of responding anesthesiologists were involved in CPP, in the majority of cases, this accounted for less than 20% of their clinical time. Thirty percent of those involved in CPP had previous training in pain management. The types of CPP included nerve blocks (84%) and pharmacological treatment (60%) in non-cancer pain (85%) and cancer pain (50%) patients. Ten percent and 28% of anesthesiologists were involved in research and teaching respectively while 26% were affiliated with a multidisciplinary clinic. The healthcare professions that anesthesiologists had access to or were directly working with in their practice were as follows: acupuncture (18%), nursing (36%), psychology (28%), psychiatry (35%) and physiotherapy (58%). Epidural steroid injection was the most commonly practiced intervention (82%). This was followed by trigger point injection (70%), stellate ganglion block (61%), occipital nerve block (60%) and lumbar sympathetic block (50%). Practice of interventional procedures was highly diverse. Seventy percent of anesthesiologists prescribed opioids as part of their CPP. However, half of them never incorporated an opioid agreement with patients. Opioids were most commonly used in the sustained release form. Conclusion: Approximately one-third of anesthesiologists surveyed incorporate chronic pain in their practice and their pattern of practice is widely diversified. Objectif : Decrire le modele de pratique en douleur chronique (PDC) des anesthesiologistes du Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".