Versorgungsnahe Daten zur Evaluation von Interventionseffekten: Teil 2 des Manuals
Bibliographic record
Abstract
The evaluation of intervention effects is an important domain of health services research. The ad hoc commission for the use of routine practice data of the German Network for Health Services Research (DNVF) therefore provides this second part of its manual focusing on the use of routine practice data for the evaluation of intervention effects. First, we discuss definition issues and the importance of contextual factors. Subsequently, general requirements for planning, data collection and analysis as well as concrete examples for the evaluation of intervention effects for the 3 fields of application regarding pharmacotherapy, nonpharmaceutical interventions as well as complex interventions are elaborated. We consider scenarios in which no information from randomized controlled trials (RCTs) comparing the two groups directly is yet available or in which RCTs are already available but an extension of the research question is required. In all examples either with or without randomization, the first and foremost question is always whether the data source is suitable for the specific research question. Most of the examples chosen are from oncology trials, because the necessary data are already available for Germany, at least in some form. Finally, the manual discusses possible challenges for future use of these data.
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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 | 0.027 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.056 |
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".