Agreement between scales for screening and diagnosis of motor development at 6 months
Bibliographic record
Abstract
OBJECTIVE: To ascertain the degree of agreement between a score for screening and another for diagnosis of motor development in 6-month old infants and to define the most appropriate cutoff point for screening. METHODS: A sectional study, enrolling asymptomatic full term newborns with gestational ages from 37 to 41 weeks, who were discharged from the maternity unit 2 days after birth and are resident in the Campinas area. Infants were excluded if they presented genetic syndromes, malformations, congenital infections, intensive care admission or low birth weight. The assessment instruments investigated were the Alberta Infant Motor Scale (AIMS) and the Bayley Scales of Infant Development II (BSID-II). Two cutoff points were evaluated for the AIMS, the 5th and 10th percentiles, and for the BSID-II infants were classified according to its motor index score (IS) as having inadequate (IS < 85, at least 1 standard deviation below the mean) or adequate performance (IS >or= 85, above the mean minus 1 standard deviation). RESULTS: The study sample comprised 43 infants. Six infants (14.00%) exhibited inadequate motor performance. Using the BSID-II motor classification and the 5th percentile AIMS cutoff, sensitivity was 100%, specificity 78.37%, accuracy 81.39%, kappa index 0.50 and p < 0.001; whereas, using the BSID-II motor classification and the 10th percentile AIMS cutoff, sensitivity was 100%, specificity 48.64%, accuracy 55.81%, kappa index 0.20 and p 0.025. CONCLUSIONS: The results suggest that concordance between the two 6-month assessment scales is good. The parameters employed are best combined using the 5th percentile AIMS cutoff point.
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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".