Validation of the Motor Functional Development Scale for Young Children to predict motor outcome in preterm infants: A 2 years follow-up study
Bibliographic record
Abstract
OBJECTIVE: To examine the validity of the Motor Functional Development Scale for Young Children (DF-mot) to predict motor developmental delays in preterm infants. METHOD: This retrospective cohort study includes 67 preterm infants who were assessed at 3-5 months by the DF-mot and the Alberta Infant Motor Scale (AIMS); and at 22-25 months by the Bayley Scales of Infant-Toddler Development (Bayley-III). The properties of the DF-mot and the AIMS were examined based on their ability to predict motor delays on the Bayley-III. RESULTS: The DF-mot gross motor subscale -2 SD and the AIMS 10th centile showed best balance between sensitivity and specificity (respectively Se = 57.1%, Sp = 71.7% and Se = 50%, Sp = 73.5%). Overall, the DF-mot fine motor subscale fails to predict motor delays. CONCLUSION: The DF-mot shows a lack of sensitivity and of positive predictive value to accurately predict motor outcome at 2 years in preterm infants. ABBREVIATIONS: CA, Corrected age; AIMS, Alberta Infant Motor Scale; DF-mot/PML, Motor Functional Development Scale for Young Children postural motor locomotor; DF-mot/EHGC, Motor Functional Development Scale for Young Children eye-hand grip coordination; Bayley-III/GM, Gross motor subscale of the Bayley Scales of Infant-Toddler Development Third Edition; Bayley-III/FM, Fine motor subscale of the Bayley Scales of Infant-Toddler Development Third Edition.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".