Bibliographic record
Abstract
Objective To provide scientific evidence for the establishment of medical specialist system in China by investigating the history, current situation, problems and countermeasures of medical specialties training at home and aboard. Methods The principle and theroy of evidence-based medicine were adopted. The information before Dec. 31, 2003 of Pubmed, CBM, official website, some journals, most frequently used search engines and medical monograph were systematically reviewed. Included literatures were assessed and graded according to the pre-defined criterias. Results A total of 1 319 studies (1 298 in English, 21 in Chinese) were included, among which only 6 were related to the classification of medical specialties. Based on the information from official website of USA, Canada, UK, Singapore, Australia and China (including HK and Taiwan), it showed that China has the largest number of medical specialties, followed by that of USA. In China, the number of medical specialties has more than that of the disciplines in clinical field, which was followed by resident training programs. Some specialties were duplicate, or not international standardized. Conclusions The classification of medical specialties should be developed consecutively, which comprehensively considered the international trend, characteristics of doctor training and the current situation. Specialties whose training program are well-established and developed should initiate firstly. Others will be put into practice gradually after being fully exprienced.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".