Valoración de movimientos generales como herramienta pronóstica de parálisis cerebral infantil en prematuros: revisión sistemática
Bibliographic record
Abstract
INTRODUCTION: Cerebral palsy is considered to be the main cause of physical disability in childhood. General movements are an assessment tool in order to predict the neurological and long-term outcome of the newborn. AIM: To analyze the current evidence on the general movements assessment in preterm infants as cerebral palsy prognostic tool. SUBJECTS AND METHODS: Systematic review following PRISMA statements. Databases consulted were: PubMed/Medline, Lilacs, IBECS, Cochrane, PEDro, Cinhal, Sport Discuss, Phyinfo, Academic Search Complete, Web of Science, and SciELO. We included studies that evaluated general movements in the first 20 weeks premature newborns. We excluded studies where the sample submit other pathologies or medication was administered. Newcastle-Ottawa Scale was used to assessment the risk of bias. RESULTS: Ten cohort studies form this review. 2243 premature, with an average of 30.9 weeks of gestation, were analyzed. General movements recording was carried out between 5 and 30 minutes. When there are abnormal general movements, the chances of neurological involvement increase during development, whereas when normal general movements are evaluated, there will rarely be a subsequent cerebral palsy diagnosis. CONCLUSIONS: The predictive validity of the preterm general movements assessment is confirmed as a tool to predict cerebral palsy early. Since preterm infants are more likely to trigger abnormal general movements, it is interesting to promote this type of assessment.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".