Bibliographic record
Abstract
I was lucky - I won a grant from the Soros Foundation for an internship in traumatology at the Austrian Hospital in Vienna. The Trauma Clinic, which consists of four 30-bed departments, is located in a huge general hospital, which can be compared to a city. On the ground floor of this "city" there are cafes, restaurants, a hairdresser, a bank, a currency exchange office, a post office, a library for employees, a library for patients, three churches (for Christians, Jews and Muslims), various shops, etc. You can live in this city for weeks, without going anywhere and without feeling the need for anything. I did not see only pharmacy kiosks. Probably they are not needed if the hospital has everything. Even "Canadian" crutches are given to all victims for free. And there are a lot of patients with injuries (together with outpatients) - up to 200 per day. Fresh fractures try to operate in the first hours after the injury. All operations on the bones are accompanied by control with the help of an image intensifier from Siemens. Any type of osteosynthesis tends to be performed through small incisions. External fixation devices are widely used, including Ilizarov devices. In general, to the name of our brilliant compatriot G.I. Ilizarov are treated with great respect. The clinic staff is multinational. The leader is Professor Vilmos Vechey, originally from Hungary. Before the tragic events of 1956 in Budapest, he wore a red tie and managed to be a pioneer. Then he emigrated with his parents to Austria, where he made a brilliant career. There are doctors from Iran, India, Ukraine on the staff.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.194 | 0.052 |
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".