Number of pathologists in Germany: comparison with European countries, USA, and Canada
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
The rapid development of pathology is in contrast to a shortage of qualified staff. The aims of the present study are to compile basic information on the numbers of German physicians in pathology and to compare it with the situation in Europe and overseas. In addition, model calculations will shed light on the effects of part-time working models. Various publicly accessible databases (EuroStat) as well as publications of medical associations and professional associations of European countries and the USA/Canada were examined. In addition, a survey was carried out among the institutes of German universities. Figures from 24 European countries and the USA/Canada were evaluated. With one pathologist per 47,989 inhabitants, the density of pathologists in Germany in relation to the population is the second-lowest in Europe (average: 32,018). Moreover, the proportion of pathologists among the physicians working in Germany is the lowest in Europe and at the same time lower than in the USA and Canada (Germany: 1:200, USA: 1:70, Canada: 1:49). The ratio of pathologists to medical specialists is shifted in the same direction. The survey among university pathologists revealed a relevant increase in the workload over the last 10 years. The majority of institutes can manage this workload only with considerable difficulties. With a ratio between specialists and residents of 1:1, the university institutes show a high commitment in the area of training. The results of this study indicate a shortage of pathologists in Germany that could lead to a bottleneck in large parts of the health system.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it