Procedures performed by general practitioners and general internal medicine physicians - a comparison based on routine data from Northern Germany
Bibliographic record
Abstract
BACKGROUND: In response to a rising shortage of general practitioners (GPs), physicians in general internal medicine (GIM) have become part of the German primary care physician workforce. Previous studies have shown substantial differences in practice patterns between both specialties. The aim of this study was to analyse and compare the application of procedures by German GPs and GIM physicians based on routine data. METHODS: -test. The selection of procedures was based on international and own preliminary studies on primary care procedures. RESULTS: In the first quarter of 2013/2015 respectively, 1228/1227 GPs and 447/484 GIM physicians provided services in Schleswig-Holstein. Significant differences were found for 20 of the 46 procedures. GPs had higher application rates of procedures concerning health screening (e.g. adolescent health examination, well-child visits) and minor surgery. GIM physicians more often applied technology-oriented procedures, such as ultrasound scans, electrocardiograms (ECG), and 24-h ambulatory blood pressure measurements. The treatment patterns of both specialities did not vary much during the study period. Cardiac stress testing was the only significantly increased GP procedure in that time. CONCLUSIONS: Our results suggest substantial differences in the application of procedures between GPs and GIM physicians with potential consequences for the overall primary healthcare provision. The findings could foster a discussion about training needs for procedures in primary care to ensure its comprehensiveness. The results reflect scope for changes in vocational training in the future for an effective and efficient re-allocation of primary healthcare.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".