Perinatal inflammation exposure and developmental outcomes 7 years after neonatal arterial ischaemic stroke
Bibliographic record
Abstract
AIM: To test the association between perinatal inflammation exposure and Full-Scale IQ (FSIQ) score 7 years after neonatal arterial ischaemic stroke (NAIS). METHOD: We conducted a cross-sectional ancillary study nested in a multicentric longitudinal French cohort of infants born at term with NAIS between November 2003 and October 2006. Seventy-three children were included (45 males, 28 females). The a priori defined primary outcome measure was the FSIQ score assessed with the Wechsler Intelligence Scale for Children, Fourth Edition at 7 years of age. RESULTS: Seventeen (23%) of the included children were exposed to perinatal inflammation. Exposure to perinatal inflammation was independently associated with an increase of FSIQ score (coefficient 13.4, 95% confidence interval 1.3-25.4; p = 0.03). Children exposed to perinatal inflammation had a higher median cerebral volume, a lower median lesion volume, and less extensive lesion distributions compared to non-exposed children. INTERPRETATION: We propose the existence of two NAIS categories: arteritis-associated NAIS in children exposed to perinatal inflammation and embolism-associated NAIS in children non-exposed to perinatal inflammation. Identifying these two NAIS categories would open the possibility for specific curative strategies: anti-inflammatory strategy in arteritis-associated NAIS and recanalization strategy in embolism-associated NAIS.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".