Bibliographic record
Abstract
비록 인공 고관절 치환술 후 발생하는 대퇴스템 주위 골절의 발생 빈도가 증가할 것으로 예상되나, 여전히 치료가 어려운 합병증의 하나로 남아있다. 이러한 골절에 대한 최선의 치료 결과를 얻기 위해서는 철저한 임상 및 방사선학적 검사, 정확한 골절 분류, 최신 치료 원칙의 이해가 필수적이다. Vancouver 분류법은 치료 계획을 수립하는 데 쉽고, 효과적이며 재현성이 높은 방법이다. 불안정한 대퇴스템과 동반된 대퇴스템 주위 골절은 재치환술을 필요로 하지만 대퇴스템이 안정적으로 고정된 경우 골접합 치료만으로 충분하다. 저자 들은 Vancouver 분류법에 따른 대퇴스템 주위 골절의 치료 원칙 및 합병증을 피하는 방법에 대해 기술하고자 한다.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".