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Record W2017378691 · doi:10.1017/s001216220500112x

Proposed definition and classification of cerebral palsy, April 2005

2005· review· en· W2017378691 on OpenAlexaff
Martin Bax, Murray Goldstein, Peter Rosenbaum, Alan Leviton, Nigel Paneth, Bernard Dan, Bo Jacobsson, Diane L. Damiano

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2005
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsCentre for Disability Prevention and Rehabilitation
Fundersnot available
KeywordsCerebral palsyPsychologyInternational Classification of Functioning, Disability and HealthMedicinePhysical medicine and rehabilitationNeuroscienceRehabilitation

Abstract

fetched live from OpenAlex

Because of the availability of new knowledge about the neurobiology of developmental brain injury, information that epidemiology and modern brain imaging is providing, the availability of more precise measuring instruments of patient performance, and the increase in studies evaluating the efficacy of therapy for the consequences of injury, the need for reconsideration of the definition and classification of cerebral palsy (CP) has become evident. Pertinent material was reviewed at an international symposium participated in by selected leaders in the preclinical and clinical sciences. Suggestions were made about the content of a revised definition and classification of CP that would meet the needs of clinicians, investigators, and health officials, and provide a common language for improved communication. With leadership and direction from an Executive Committee, panels utilized this information and have generated a revised Definition and Classification of Cerebral Palsy. The Executive Committee presents this revision and welcomes substantive comments about it.

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)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0120.009
Science and technology studies0.0010.004
Scholarly communication0.0030.004
Open science0.0050.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.003

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 head0.060
GPT teacher head0.308
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2,591
Published2005
Admission routes1
Has abstractyes

Explore more

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