Prescriber adherence to guidelines for chronic noncancer pain management with opioids: Systematic review and meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: This review quantified prescriber adherence to opioid prescribing guidelines for chronic noncancer pain (CNCP). METHOD: We searched CINAHL, Embase, MEDLINE, PsycINFO, the Cochrane Library, and the Joanna Briggs Institute EBP Database from inception until June 3, 2019. Studies that focused on provider adherence to opioids guidelines for CNCP in North America were eligible. Four reviewers screened studies, extracted data, and assessed study quality. RESULTS: = 22,512 patients). Survey data indicated that adherence was 49% (95% CI [40, 59]) for treatment agreements, 33% (95% CI [19%, 47%]) for urine drug testing, 48% (95% CI [26%, 71%]) for consultation with drug monitoring program, 57% (95% CI [35%, 79%]) for assessing risk of aberrant medication-taking behavior, and 61% (95% CI [35%, 87%]) for mental health screening. Chart review data indicated that the proportion of patients with documentation was 40% (95% CI [29, 51]) for treatment agreements, 41% (95% CI [32%, 50%]) for urine drug testing, 40% (95% CI [2%, 78%]) for consultation with drug monitoring program, 41% (95% CI [20%, 64%]) for assessing risk of aberrant medication-taking behavior, and 22% (95% CI [9%, 33%]) for mental health screening. Year of publication, practice guideline referenced, and risk of bias explained significant heterogeneity. No study evaluated whether nonadherence to recommendations reflected well-justified deviations to care. CONCLUSIONS: Adherence to guideline recommendations for opioids for CNCP is low. It is unclear whether nonadherence reflects thoughtful deviations in care. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".