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Record W1104547202 · doi:10.1177/0883073815596610

Age at Referral of Children for Initial Diagnosis of Cerebral Palsy and Rehabilitation: Current Practices

2015· article· en· W1104547202 on OpenAlexafffund
Lara Hubermann, Zachary Boychuck, Michael Shevell, Annette Majnemer

Bibliographic record

VenueJournal of Child Neurology · 2015
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsMcGill UniversityMontreal Children's HospitalCentre for Interdisciplinary Research in RehabilitationMcGill University Health CentreUniversité de Montréal
FundersCanadian Institutes of Health ResearchCentre for Interdisciplinary Research in Rehabilitation
KeywordsCerebral palsyReferralRehabilitationPhysical medicine and rehabilitationMedicinePediatricsNeurological rehabilitationPhysical therapyFamily medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: This study describes current practices in the age at referral for diagnosis of cerebral palsy and factors that influence earlier referral. STUDY DESIGN: Retrospective chart review (2002-2012). RESULTS: Of 103 children referred for diagnosis, 81 were referred to a neurologist by other medical specialists at a mean of 13.6 ± 15.7 months, whereas primary care providers referred much later (mean = 28.8 ± 27.1 months). Children admitted to the neonatal intensive care unit were referred earlier (mean = 9.3 ± 10.2 months) than those not (28.1 ± 24.9 months). Referral to rehabilitation was similarly delayed. CONCLUSIONS: Primary care providers generated a minority of referrals, of concern given their role in developmental surveillance. Remarkably high variability suggests knowledge of cerebral palsy attributes varies widely among service providers. Half of children with cerebral palsy do not have a complicated birth history; subsequently, referrals for diagnosis and management are often delayed. New strategies are needed to optimize prompt referral by primary care providers.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.047
GPT teacher head0.344
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations103
Published2015
Admission routes2
Has abstractyes

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