Opioid Calculators : Quality or Just Quantity?
Bibliographic record
Abstract
Background and AimsThere are a vast number of opioid calculators available to use online. These calculators have been demonstrated to lack consistency in opioid equivalence. The aim of this project was to assess the quality of the most commonly used online opioid calculators and whether their use can be recommended.MethodsOnline opioid calculators were found using the Google search engine, searching for the terms u201copioid calculatoru201d and u201cmorphine conversionu201d. The searches were performed for the 6 main English language speaking regions, United Kingdom, United States, Ireland, Canada, Australia and New Zealand. The first 10 opioid calculators that were found in each region using each of the search terms were considered. The AGREE II instrument was applied to the calculators in order to assess the quality of the calculators. To assess whether a calculator could be recommended for use in clinical practice, the calculator would have to score at least 75% across all domains.Results13 online opioid calculators were found. Using the AGREE II instrument, the scores across the domains ranged from 0% to 72.2%. No calculator scored greater than 50% across all domains for quality.ConclusionsThe commonly used calculators available have now been shown to lack quality and well as consistency. This study has found that online opioid calculators cannot be recommended for use in clinical practice.
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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 | 0.035 | 0.228 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".