Current Therapeutic Management of Perinatal Stroke with a Focus on the Upper Limb: A Cross-Sectional Survey of UK Physiotherapists and Occupational Therapists
Bibliographic record
Abstract
AIM: To determine current UK pediatric physiotherapist (PT) and occupational therapist (OT) management of perinatal stroke. DESIGN: Web-based cross-sectional survey. METHODS: Participants were members of the Association of Paediatric Chartered Physiotherapists and Occupational Therapists specialist section: children young people and families working with infants. Items covered prioritization of referrals, assessments, therapy approaches aimed at the upper limb, and parental support. RESULTS: 179 therapists responded. 87.2% of PTs and 63.0% of OTs managed infants with perinatal stroke. Infants with clinical signs of motor dysfunction at referral were prioritized for early initial assessment. The most frequently used assessments were the Alberta Infant Motor Scale (AIMS) and Bayley Scales of Infant Development (BSID). Of PTs and OTs, 41.9 and 40.0% used no standardized assessments. Frequently used therapy interventions were Bobath/Neurodevelopmental Therapy (NDT), positioning aids and passive movements. 88.1% of therapists would choose a bilateral rather than unilateral (affected side) therapy approach for infants with perinatal stroke aged up to 6 months. Of PTs and OTs, 56.9 and 57.1% provided psychological support to families. CONCLUSIONS: Assessment and provision of therapy services following perinatal stroke is variable. Increased use of standardized assessments and centralized data collection regarding service provision for high-risk infants is recommended.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".