Independent Walking After Neonatal Arterial Ischemic Stroke and Sinovenous Thrombosis
Bibliographic record
Abstract
Few studies have examined walking after neonatal arterial ischemic stroke and sinovenous thrombosis. We looked at the development of walking in a retrospective and consecutive cohort study of 88 term and near-term neonates. We used Kaplan-Meier survival curves and Cox proportional hazards models to assess (1) sex, (2) stroke type (arterial ischemic stroke or sinovenous thrombosis), (3) number of cerebral hemispheres with infarction, and (4) presence of neonatal comorbidity as predictors of the probability over time of starting to walk independently. These variables were assessed as predictors of parent-reported gait normality using the chi-square test on 2 x 2 contingency tables. Seventy-five of 83 survivors (90.4%, 95% confidence interval = 81.9-95.7) walked with a median time of first steps at 13 months of age (95% confidence interval = 12-14). Only bilateral strokes were associated with a lower probability over time of initiating independent walking (hazard ratio = 0.41, P = .04). Parents reported normal gait for 58 of 75 walkers (77.3%, 95% confidence interval = 67.8-86.8). No variables predicted parent-reported gait normality. Our findings suggest that most survivors of neonatal arterial ischemic stroke and sinovenous thrombosis walk with a gait that appears normal to parents, but bilateral infarctions decrease the probability over time of starting to walk independently.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".