TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation
Bibliographic record
Abstract
최근 인공지능(AI) 방법, 특히 머신러닝의 발전에 따라 예측 모델 개발에 대한 관심과 투자 규모가 크게 증가하고 있다.예측 모델 연구가 실제 사용자에게 가치 있게 활용되기 위해서는, 연구자가 왜 연구를 수행했는지, 무엇을 했는지, 그리고 어떤 결과를 얻었는지를 투명하고 완전하며 정확하게 기술해야 한다.TRIPOD 지침의 개정판은 AI 방법을 적용한 예측 모델 연구 전반을 일관성 있게 안내하고, 회귀 분석이든 머신러닝이든 적용 방법에 관계없이 모두를 아우르는 지침을 제공한다.TRIPOD+AI 지침은 27개 항목의 체크리스트, 각 항목별 보고 권고사항을 상세히 설명하는 확장 체크리스트, 그리고 13개 항목의 초록 전용 TRIPOD+AI 체크리스트로 구성된다
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 | 0.039 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.036 | 0.047 |
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".