Language and Fine Motor Outcomes of Children Born Prematurely: Hidden Deficits.
Bibliographic record
Abstract
The goal of this study was to provide preliminary data on fine motor and language outcomes of children born prematurely in a Northern Ontario, Canada, hospital. Participants (n=91) had a mean gestational age of 31.0 wks (SD 2.8) and a mean birth weight of 1.65 kg (SD 0.53). A retrospective chart review was conducted on all children monitored by an interdisciplinary Follow-Up Program, assessed by the same clinician from 0-2 years. Overall, the results demonstrated that the majority of children assessed fell within the average range in their fine motor development; a greater incidence of delays were noted in language development. The largest proportion of children referred to speech-language therapy were born very preterm and/or with low birthweight; those referred to occupational therapy were most often born late preterm and/or with low birthweight. At the time of the last appointment, approximately 30% of participants had delays in both fine motor and language development, the largest proportion born late preterm, those historically perceived to be at lower risk. Of these, 100% had been previously identified as delayed in language and 40% in fine motor development. These results demonstrate the high prevalence, low morbidity deficits in all categories of premature children. Given the significant relationship between motor development, social cognition, language and social interactions, the early identification and referral of these children is imperative.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".