Predictive models of early motor development in preterm infants: a longitudinal-prospective study
Bibliographic record
Abstract
Introduction: Preterm infants are vulnerable to developmental delays. Detecting problems at an early age is one of the challenges of professionals and researchers in the area.Objectives: To analyse the motor development and to identify the risk factors associated with predictors of overall and motor delay in preterm newborns.Methods: Eighty preterm infants (50% female; mean gestational age = 33 ± 2.2 weeks) with low birth weight (average of 1,715 ± 437 g) were evaluated using the Neurobehavioral Assessment of the Preterm Infant (NAPI) during the neonatal phase (prior to term age), the Denver Developmental Screening Test II (DDST-II) between 2 and 8 months, the Test of Infant Motor Performance between 2 and 4 months regarding motor development and the Alberta Infant Motor Scale between 4 and 8 months. Results: Neurobehavioural delay was noted in 24% of the infants in the neonatal phase. Between 2 and 8 months, the delay in overall development was ≥ 31% and the delay in motor development was 35–36 %. Decreased levels of alertness, orientation, motor developmentand vigouraccording to theNAPIwereshown to be predictive of a delay indevelopment between4 and 6 months of age.The delayin overall development between 2 and 6 months was predictive of a delay in motor development between6 and 8months. Conclusion: Neurobehavioural variables, hospital stayandoverall delayare goodpredictorsof motor developmentduring the first yearof age.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".