Bibliographic record
Abstract
Iván Kiss was born in the South-Hungarian town Pécs in 1945. He studied at the University Medical School, Pécs between 1963-1969. After a specialisation in pathology, he qualified also in anesthesiology at the Postgraduate Medical Institute, Budapest. After leaving Hungary in 1977 he gained a position at the University Hospital, Freiburg, Germany, soon appointed as head of the anesthesiology unit. In 1987 he attained the “Doctor Medicinae Habilitatus” degree upon his thesis on cancer pain. Later this work was partly published in “Pain, 29: 195-207 1997” the leading journal of the subject with the title “The McGill Pain Questionnaire - German version. A study on cancer pain”. In 1989 he moved to Essen for holding the job of the chief anesthesiologist at the Krupp-Clinic. By that time, he was acknowledged as an internationally recognized expert in anaesthesiology and pain therapy. His expertise was honoured by the „Doctor honoris causa” title at the Semmelweis University, Budapest in 1999. Dr. med. habil. Iván Kiss died on November 29, 2017. In his person, we lost a colleague and friend, a devoted teacher and researcher, and a committed clinician. We keep him in our memories as a real Paragon of the medical vocation.
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.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.040 | 0.034 |
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".