Motor testing at 1 year improves the prediction of motor and mental outcome at 2 years after perinatal hypoxic–ischaemic encephalopathy
Bibliographic record
Abstract
AIM: To investigate the predictive value of motor testing at 1 year for motor and mental outcome at 2 years after perinatal hypoxic-ischaemic encephalopathy (HIE) in term neonates. METHOD: Motor and mental outcome at 2 years was assessed with the Bayley Scales of Infant Development, 2nd edition (BSID-II) in 32 surviving children (20 males, 12 females; mean gestational age 40.2 wk, SD 1.4; mean birthweight 3217g, SD 435) participating in a prospective cohort study of HIE. The predictive value of three motor tests (Alberta Infant Motor Scale [AIMS], BSID-II, and the Neurological Optimality Score [NOS]) at 1 year was analysed, in addition to predictions based on neonatal Sarnat staging and magnetic resonance imaging (MRI). Poor motor test results were defined as an AIMS z-score of <-2, a psychomotor developmental index of the BSID-II of <70, or a NOS of <26. Poor motor and poor mental outcome at 2 years was defined as a psychomotor developmental index or mental developmental index of the BSID-II of <70. RESULTS: Twelve children, all with Sarnat grade II, had a poor motor outcome and 12 children, of whom one had Sarnat grade I, had a poor mental outcome at 2 years. Nine children had cerebral palsy, of whom five had quadriplegia, three had dyskinesia, and one had hemiplegia. Poor motor tests at 1 year increased the probability of a poor motor outcome from 71% (range 92 to 100%), and a poor mental outcome from 59% (range 77 to 100%) in children with Sarnat grade II and abnormal MRI, assessed with the AIMS and BSID-II or NOS respectively. INTERPRETATION: Additional motor testing at 1 year improves the prediction of motor and mental outcome at 2 years in children with Sarnat grade II and abnormal MRI.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".