Saxon Epidemiological Study in General Practice-6 (SESAM-6): protocol of a cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: General practitioners (GPs) are mostly the first point of contact for patients with health problems in Germany. There is only a limited epidemiological overview data that describe the GP consultation hours based on other than billing data. Therefore, the aim of Saxon Epidemiological Study in General Practice-6 (SESAM-6) is to examine the frequency of reasons for encounter, prevalence of long-term diagnosed diseases and diagnostic and therapeutic decisions in general practice. This knowledge is fundamental to identify the healthcare needs and to develop strategies to improve the GP care. The results of the study will be incorporated into the undergraduate, postgraduate and continuing medical education for GP. METHODS AND ANALYSIS: This cross-sectional study SESAM-6 is conducted in general practices in the state of Saxony, Germany. The study design is based on previous SESAM studies. Participating physicians are assigned to 1 week per quarter (over a survey period of 12 months) in which every fifth doctor-patient contact is recorded for one-half of the day (morning or afternoon). To facilitate valid statements, a minimum of 50 GP is required to document a total of at least 2500 doctor-patient contacts. Univariable, multivariable and subgroup analyses as well as comparisons to the previous SESAM data sets will be conducted. ETHICS AND DISSEMINATION: The study was approved by the Ethics Committee of the Technical University of Dresden in March 2023 (SR-EK-7502023). Participation in the study is voluntary and will not be remunerated. The study results will be published in peer-reviewed scientific journals, preferably with open access. They will also be disseminated at scientific and public symposia, congresses and conferences. A final report will be published to summarise the central results and provided to all study participants and the public.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.031 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".