Routine skin cancer screening in Germany: First data on the impact on health care in dermatology
Bibliographic record
Abstract
BACKGROUND: On July 1st 2008, community skin cancer screening (cSCS) was established in routine care of the German statutory health insurances (SHI). This study aimed at gathering preliminary data on the impact of community SCS on health care provision in German dermatology practices. PATIENTS AND METHODS: Standardized questionnaires were sent to about 2,000 German dermatology practices. Data were analyzed descriptively and with bivariate tests. RESULTS: In total, 693 (34.7%) questionnaires were returned. Each practice performed an average of 354 SCS per quarter, the mean payment being euro 21.50. About 78% named an increase in SCS with an average increase of 36.7%. About 54% of practices performed SCS under SHI payment combined with individual health services paid by the patients ("IGeL"), 38% only as SHI and 8% exclusively as "IGeL". In 85% of practices, the number of surgical procedures had increased since the start of community SCS. 40% had an increase in drug prescriptions related to SCS. 32% were satisfied with SCS, while 40% were unsatisfied. 29 % would prefer SCS only as covered by SHI, 29% only as IGeL, and 42% in a combined fashion. 70% regarded the quality of health care of patients with skin cancer in Germany better since the introduction of cSCS. CONCLUSIONS: Dermatologists in Germany have mainly accepted their role in providing skin cancer as standard care covered by SCS. The regulatory and economic conditions for this need further improvement.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".