EFFECTIVENESS OF A REGIONAL REPORTING PROGRAM IN IMPROVING QUALITY OF ADVERSE DRUG REACTION CASE REPORTS
Bibliographic record
Abstract
The Canadian voluntary adverse drug reaction (ADR) reporting program is an established surveillance method for monitoring drug safety, using case report data for signaling the occurrence of new or unexpected adverse drug reactions in a timely manner. In 1990, as part of improvements to the Canadian system of post-marketing drug surveillance, a pilot regional ADR reporting program (SaskADR) was developed in the province of Saskatchewan to investigate whether ADR reporting could be enhanced through decentralintion of the national program. \nDuring the first two years of SaskADR operation, there was a four-fold increase in the annual number of reports submitted by Saskatchewan practitioners to SaskADR as compared to the national program. The purpose of this research was to evaluate whether implementation of the SaskADR program not only improved the quantity of ADR reports, but also improved the quality of information documented on the ADR case reports. \nComparisons of ADR case report quality were made between 566 case reports submitted by Saskatchewan health professionals to the SaskADR program during the first two years of operation and 281 case reports submitted by Saskatchewan health professionals to the national ADR reporting program in the four years prior to implementation of SaskADR. The methodology for this research involved the development of indicators and criteria for the measurement of case report quality, which \nreflected the purpose and function of voluntary ADR reporting programs. \nImplementation of the SaskADR reporting program was associated with an improvement in the quality of ADR case report data in comparison to case reports submitted to the national program. The SaskADR program demonstrated an increased reporting of "important reactions" or reactions which are serious or unexpected, or occur with a newly marketed drug. Information useful for characterization of the reaction and assessment of drug causality were better documented in the SaskADR reports. In addition, information considered essential for the submission of a valid ADR case report was more complete on the SaskADR reports. \nImprovement in the quality of ADR information enhances the utility of the case report submissions in meeting the goals and objectives of the voluntary ADR reporting program. Demonstration of an improved quality of case reports, in combination with an increased rate of reporting, supports the development of regional ADR reporting centres as a mechanism of improving the Canadian voluntary ADR reporting program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".