Optimizing Antibiotic Prescribing for Acute Respiratory Tract Infection in German Primary Care: Study Protocol for Evaluation of the RESIST Program
Bibliographic record
Abstract
BACKGROUND: The emergence and increased spread of microbial resistance is a major challenge to all health care systems worldwide. In primary care, acute respiratory tract infection (ARTI) is the health condition most strongly related to antibiotic overuse. OBJECTIVE: The RESIST program aims at optimizing antibiotic prescribing for ARTI in German primary care. By completing a problem-orientated online training course, physicians are motivated and empowered to utilize patient-centered doctor-patient communication strategies, including shared decision making, in the treatment of patients with ARTI. METHODS: RESIST will be evaluated in the form of a nonrandomized controlled trial. Approximately 3000 physicians of 8 (out of 16) German federal states can participate in the program. Patient and physician data are retrieved from routine health care data. Physicians not participating in the program serve as controls, either among the 8 participating regional Associations of Statutory Health Insurance Physicians (control group 1) or among the remaining associations not participating in RESIST (control group 2). Antibiotic prescription rates before the intervention (T0: 2016, 1st and 2nd quarters of 2017) and after the intervention (T1: 3rd quarter of 2017 until 1st quarter of 2019) will be compared. The primary outcome measure is the overall antibiotic prescription rate for all patients insured with German statutory health insurance before and after provision of the online course. The secondary outcome is the antibiotic prescription rate for coded ARTI before and after the intervention. RESULTS: RESIST is publicly funded by the Innovations funds of the Federal Joint Committee in Germany and was approved in December 2016. Recruitment of physicians is now completed, and a total of 2460 physicians participated in the intervention. Data analysis started in February 2020. CONCLUSIONS: With approximately 3000 physicians participating in the program, RESIST is among the largest real-world interventions aiming at reducing inadequate antibiotic prescribing for ARTI in primary care. Long-term follow up of up to 21 months will allow for investigating the sustainability of the intervention. TRIAL REGISTRATION: ISRCTN Registry ISRCTN13934505; http://www.isrctn.com/ISRCTN13934505. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/18648.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".