Age at referral for diagnosis and rehabilitation services for cerebral palsy: a scoping review
Bibliographic record
Abstract
AIM: This study sought to: (1) determine what is known about age at referral for diagnosis and rehabilitation services for children suspected of having cerebral palsy (CP); and (2) identify factors associated with earlier referral. METHOD: A scoping review was conducted to summarize existing literature. We systematically searched Allied and Complementary Medicine, CINAHL, Cochrane Library, Embase, and PsycINFO for evidence published between 1979 and 2017 on age at referral for diagnosis or age at referral to rehabilitation services for children suspected of having CP. Quantitative and thematic analyses of the literature were performed. RESULTS: Our search yielded 777 articles, of which 15 met the inclusion criteria. Only one study focused on age at referral for diagnosis of CP (mean 16.6mo±19.2mo), with two on age at referral to rehabilitation services (means 13.9mo±15.8mo and 12.4mo). Potential predictors of earlier referral identified include referral source, type of CP, and a complicated birth history. INTERPRETATION: Evidence is sparse; however, available studies suggest high variation in the age at which children are being referred for diagnosis, typically ranging from 10 months to 21 months. Evidence indicates that subgroups of children with CP might be experiencing prolonged delays. Findings highlight the need to better understand what contributes to delays in referral for diagnosis and rehabilitation. WHAT THIS PAPER ADDS: Evidence on age at referral for diagnosis of cerebral palsy is sparse. Potential predictors of delayed referral represent targets to minimize delays in diagnosis. A subset of children may be experiencing unnecessary delays in referral.
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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 | 0.015 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".