Feasibility and validity of using healthcare databases to conduct cross‐national comparative studies of opioid use, its determinants and consequences
Bibliographic record
Abstract
PURPOSE: A cross-national comparative (CNC) study about opioid utilization would allow the identification of strategies to improve pain management and mitigate risk. However, little is known about the accessibility and validity of information in healthcare databases internationally. This study aimed to identify the feasibility of using healthcare databases to conduct a CNC study of opioid utilization and its associated consequences. METHODS: A cross-sectional survey was launched in March 2018, including experts interested in CNC studies comparing opioid utilization by purposeful sampling. An electronic survey was used to collect database characteristics, medicine information, and linkage information of each aggregate-level dataset (AD) and individual patient-level dataset (IPD). RESULTS: Overall, participants from 21 geographical regions reported 18 ADs and 19 IPDs. Information on dispensed medications is available from 17 ADs and 17 IPDs. Of the 16 ADs that include primary care settings, only 9 ADs can obtain information from secondary care settings. Fourteen IPDs included patients' characteristics or could be retrieved from linkage databases. Although most ADs are publicly accessible (n = 13), only five IPDs can be accessed without extra cost. CONCLUSION: Most ADs could be used to report opioid utilization in a primary care setting. IPDs with linkage databases should be applied to identify potential determinants, clinical outcomes, and policy impact. Data access restrictions and governance policies across jurisdictions can be challenging for timely analysis and require further collaboration.
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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 | 0.463 | 0.541 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| 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".