Bibliographic record
Abstract
A questionnaire survey was performed in order to see the current trends of the neurology training program in Japan. A questionnaire was sent out to 81 neurology program directors of the medical schools and large hospitals. 72 program directors answered the questionnaires. According to the summed results, each program had an average of 37 inpatient beds, 7 teaching staffs with the neurology board certification. The program had an average of 4 residents annually, and they served as junior neurology residents for 1.8 years, and as chief residents for 1 year with 6 months of hospital consultation. 1.4 years training in the internal medicine was prerequisite for the neurology program. The training of clinical neurophysiology was done mainly by the own faculty staff in each program, but the training of neuroradiology and neuropathology varied. A quarter of the training programs had their own teaching staffs of neuroradiology and the rest of three quarters asked for training to the neuroradiology department. 32 of 72 programs had their own teaching neuropathologists and 26 programs asked the training in the pathology department and 14 programs did not have any teaching staffs of neuropathology. It seems that these numerical data are quite similar to those of the American standard of Accreditation Councils. We must still improve the real contents of the neurology training program with more capable teaching staffs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".