Reliability of Video-Media Neurological Assessment Using the Alberta Infant Motor Scale
Bibliographic record
Abstract
本研究の目的は本邦におけるAlberta Infant Motor Scale（AIMS）の信頼性を検証することである。対象者は生後2ヵ月から17ヵ月までの健常児32名でありAIMSの方法に従って評価された。評価者を学生3名と理学療法士1名とし，評価者にAIMSの評価方法が説明された。その後，対象児の姿勢・運動が記録されたビデオを見て評価を行った。評価者間信頼性と評価者内信頼性を級内相関係数と測定の標準誤差を用いて解析した。その結果，評価者間信頼性と評価者内信頼性はいずれもICC=.99を示し，学生が評価しても正確な評価が可能であることが示唆された。1）評価者間信頼性と評価者内信頼性はともに高かったこと。2）未経験の評価者でもビデオ観察を通じてほぼ正確に評価することが可能であることが示唆された。
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".