Gross Motor Outcomes of Children Born Prematurely in Northern Ontario and Followed by a Neonatal Follow-Up Programme
Bibliographic record
Abstract
Purpose: The developing brain of a premature infant is vulnerable to injury. As a result, the long-term consequences of a premature birth include motor deficits, cognitive and behavioural problems. It is crucial to identify motor dysfunction during the preschool period because it interferes with a child's ability to explore the world. The goals of this study were to (1) provide preliminary data on the gross motor outcomes of children born prematurely and (2) determine the proportion and characteristics of the children who had maintained delays over the course of follow-up. Method: A retrospective chart review was conducted on all infants monitored by a neonatal follow-up programme. Each child was assessed by a single physiotherapist from birth until age 2 years. Of the 107 cases identified, 97 individuals were retained for analysis; they had a mean gestational age of 31.1 (SD 2.9) weeks and a mean birth weight of 1.66 (SD 0.53) kilograms. Results: The majority of children assessed were found to have gross motor outcomes in the average range. Children with scores below the average range were most often born very preterm (VPT) or moderately preterm (MPT), with very low or low birth weight, respectively. A total of 17 participants were referred to physiotherapy to address the gross motor delays identified in the follow-up programme; 14 of these 17 had previously been identified as delayed and were being monitored. Late preterm (LPT) children (n=6) were most often referred, followed by those born extremely preterm (EPT) and VPT (n=4). In total, 56 children were identified as delayed at one assessment point but were found to be within normal limits by the end of the follow-up period. Conclusion: It is important to periodically monitor premature children. A longitudinal, population-based study is also needed to provide more data on the predictors and long-term motor outcomes of MPT and LPT children.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".