Data transparency for building a stronger healthcare system: A case study from Argentinean administrative drug utilization data sources
Bibliographic record
Abstract
In order to compile an inventory of national data sources for drug utilization research (DUR) in Argentina and to verify publicly available data sources, we performed a cross-sectional study that sought to identify national and provincial databases of drug use. In July 2020, we searched the websites of government institutions, carried out a systematic query of bibliographic databases for "drug utilization research" conducted in Argentina, and conducted a survey with local experts. Data collected included: the institution responsible for the database, population covered, accessibility, source of the data, healthcare setting, geographic information, and whether data were individual or aggregated. Descriptive analyses were then performed. We identified 31 data sources for DUR; only one was publicly and conveniently accessible. Five published aggregated data and provide more detailed access by formal request. Only seven sources (23%) reported national data, and most (n=29) included only data from the public healthcare sector. Although data sources for DUR have been found in Argentina, limited access by researchers and policymakers is still an significant obstacle. Increasing health data transparency by making data sources publicly available for the purpose of analyzing public health information is crucial for building a stronger health system.
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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 | 0.084 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".