Manual für Methoden und Nutzung versorgungsnaher Daten zur Wissensgenerierung
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
" There are more and more good reasons for using existing care data, with the focus in particular on the use of register data. The associated, clearly structured methodological procedure has so far been insufficiently combined, prepared and presented transparently. The German Network for Health Services Research (DNVF) has therefore set up an ad hoc commission for the use of routine practice data (RWE/RWD). The rapid report prepared by IQWiG on the scientific development of concepts for "generation of care-related data and their evaluation for the purpose of benefit assessment of medicinal products according to § 35a SGB V" is an essential step for the use of register data for the generation of evidence. The "Memorandum Register - Update 2019" published by DNVF 2020 also describes the requirements and methodological foundations of registers. Best practice examples from oncology, which are based on the uniform oncological basic data set for clinical cancer registration (§ 65c SGB V), show, for example, that guidelines can be checked and recommendations for guidelines and necessary interventions can be derived in the sense of knowledge-generating health services research using register data. At the same time, however, there are no clear quality requirements and structured formal and content-related procedures in the areas of data consolidation, data verification and the use of specific methods depending on the question at hand. The previously inconsistent requirements are to be revised and a method guide for the use of suited data is to be developed and published. The first chapter of the manual on methods of care-related data explains the objective and structure of the manual. It explains why the use of the term "routine practice data" is more effective than the use of the terms Real Word Data (RWD) and Real World Evidence (RWE). By avoiding the term "real world" it should be emphasized in particular that high-quality research can also be based on routine practice data (e. g. register-based comparative studies).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.016 |
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 it