Development and application of indicators for the reduction of potentially preventable hospital admissions related to medications
Bibliographic record
Abstract
OBJECTIVE: The Dutch HARM-Wrestling (HW) Task Force issued general and drug-specific recommendations aimed at reducing hospital admissions related to medication (HARMs). This study examines if the drug-specific recommendations could be converted into indicators that could be monitored in existing databases of general practitioner (GP) or community pharmacy (CP) data. The study also assesses the performance of these indicators before and during the official release of HW recommendations. METHODS: HW recommendations were divided into sub-recommendations. We studied to what extent these were measurable as indicators based on available information in both databases. For each measurable indicator, performance between 2007 and 2010 was determined and possibilities for further improvement were estimated. RESULTS: Thirty-four drug-specific HW recommendations were divided into 69 sub-recommendations, 32 of which were measurable as indicator in at least one of the databases. Application of these indicators between 2007 and 2010 showed that many of the indicators did not change over time. Possibilities for further improvement were estimated as moderate to major for 16/31 (52%) indicators measured in the GP database and 6/15 (40%) HW indicators measured in the CP database. CONCLUSIONS: Further implementation of the HW recommendations and development of additional monitoring methods are warranted to improve drug safety in outpatients.
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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 | 0.056 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".