Bibliographic record
Abstract
In accordance with the government`s plan to expand the national health insurance (NHI) coverage for severe diseases such as cancer, heart disease, cerebrovascular disease, and rare and incurable disease, the diagnostic ultrasound services have been covered by NHI from October 1, 2013. The quality is very important factor in providing diagnostic services because they influence on the diagnosis, treatment, and outcome of diseases. In particular, equipments and health care providers plays an important role in providing qualitative services. The purpose of this paper is to examine the major feature of ultrasound services covered by health security system and to review quality assurance policies in other countries such as Australia, Japan, the USA, and Canada. In addition, we assessed the implication of those policies. We especially put emphasis on the types and qualifications of healthcare professionals and measures to manage equipments. All countries have reviewed on policies to promote the quality such as educational requirements of professionals or restrictions on the duration of equipment usage. Various measures should be implemented to assure the qualitative ultrasound service.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.020 |
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".