Early prognostic value of qualitative general movements assessment for motor development outcomes in neonatal purulent meningitis
Bibliographic record
Abstract
Objective To study the predictive validity of general movements (GMs) assessment for motor development outcomes in neonatal purulent meningitis. Methods According to the inclusion criteria,excluding other cerebral injury diseases,a total of 34 cases of neonatal purulent meningitis, who took part in our follow-up clinic after discharged from neonatal intensive care unit of the Second Affiliated Hospital of Wenzhou Medical University from December 2012 to December 2013, were confirmed as the participants. Infants' GMs recordings during writhing movements period (at least once at 0-3 months) and fidgety movements period (at least once at 3-5 months) were collected and assessed. Motor development outcomes was determined at least after one year old with Alberta Infant Motor Scale(AIMS). Predictive validity in each period were calculated with AIMS results as the golden standard. Results Among the 34 cases, there were 24 males and ten females, and four preterm infants and 30 term babies.The age at follow-up was from 12 months to 23 months old. Ultimately, one (3%) infant with spastic cerebral palsy, seven (21%) with motor retardation and 26 (76%) with normal motor development were diagnosed. The sensitivity, specificity, positive predictive value, negative predictive value, Youden index and residual error rate for the writhing movements period in prediction of motor development outcomes were 87.5%,46.2%,33.3%,95.6%,33.7% and 12.5%, respectively. And the corresponding figures for the fidgety movements period were 87.5%, 88.5%, 70.0%, 95.8%,76.0% and 12.5%, respectively. Conclusions GMs assessment could be an accurate predictive tool for later motor developmental outcomes in neonates with purulent meningitis, especially in fidgety movements period. Key words: Meningitis, bacterial; Motor activity; Infant behavior; Child development; Motor skills
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".