The Pediatric Stroke Outcome Measure
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Pediatric Stroke Outcome Measure (PSOM) is an objective, disease-specific outcome measure containing 115 test items suitable for newborn to adult ages. The PSOM measures neurological deficit and function across 5 subscales: right sensorimotor, left sensorimotor, language production, language comprehension, and cognitive/behavior yielding a final 10-point deficit score. The goal of this study was to examine PSOM construct validity in measuring neurological outcome in pediatric stroke survivors and interrater reliability (IRR) for both prospective and retrospective scoring. METHODS: For construct validity, PSOM subscale scores were correlated with scores on standardized neuropsychological measures matched by functional domain. We assessed IRR by comparing same-day "live" PSOM scores from 2 independent raters in 10 children (prospective IRR) and by comparing PSOM scores estimated from medical dictations across 5 raters in another 10 children (retrospective IRR). RESULTS: We analyzed PSOM scores from 203 children with ischemic stroke. PSOM subscales show good construct validity (ρ=0.2-0.4; P<0.05). PSOM subscale scores of normal/abnormal demonstrate strong agreement for domain-matched neuropsychology scores (alternative chance-corrected statistic=0.4-0.8). IRR was excellent with the 2 prospective raters' scores in almost perfect agreement (intraclass correlation coefficient, 0.93; 95% CI, 0.76-0.98). Retrospective IRR demonstrated strong agreement with an intraclass correlation coefficient of 0.77 (95% CI, 0.56-0.92). CONCLUSIONS: The PSOM is a valid and reliable outcome measure for pediatric stroke. It is useful for retrospective scoring from health records and prospective serial longitudinal outcome assessments and is ideally suited for prospective clinical trials in pediatric stroke.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".