Motor Function Test Reliability During the NeuroNEXT Spinal Muscular Atrophy Infant Biomarker Study
Bibliographic record
Abstract
BACKGROUND: The NeuroNEXT SMA Infant Biomarker Study, a two year, longitudinal, multi-center study of infants with SMA type 1 and healthy infants, presented a unique opportunity to assess multi-site rater reliability on three infant motor function tests (MFTs) commonly used to assess infants with SMA type 1. OBJECTIVE: To determine the effect of prospective MFT rater training and the effect of rater experience on inter-rater and intra-rater reliability for the Test of Infant Motor Performance Screening Items (TIMPSI), the Children's Hospital of Philadelphia Infant Test of Neuromuscular Disorders (CHOP-INTEND) and the Alberta Infant Motor Scale (AIMS). METHODS: Training was conducted utilizing a novel set of motor function test (MFT) videos to optimize accurate MFT administration and reliability for the study duration. Inter- and intra-rater reliability of scoring for the TIMPSI and inter-rater reliability of scoring for the CHOP INTEND and the AIMS was assessed using intraclass correlation coefficients (ICC). Effect of rater experience on reliability was examined using ICC. Agreement with 'expert' consensus scores was examined using Pearson's correlation coefficients. RESULTS: Inter-rater reliability on all MFTs was good to excellent. Intra-rater reliability for the primary MFT, the TIMPSI, was excellent for the study duration. Agreement with 'expert' consensus was within predetermined limits (≥85%) after training. Evaluator experience with SMA and MFTs did not affect reliability. CONCLUSIONS: Reliability of scores across evaluators was demonstrated for all three study MFTs and scores were reproducible on repeated administration. Evaluator experience had no effect on reliability.
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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 | 0.039 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".