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Record W1507595020 · doi:10.1002/art.20397

Normal scores for nine maneuvers of the Childhood Myositis Assessment Scale

2004· article· en· W1507595020 on OpenAlexaff
Robert M. Rennebohm, Karla Jones, Adam M. Huber, S. Ballinger, Suzanne L. Bowyer, Brian M. Feldman, Jeanne E. Hicks, Ildy M. Katona, Carol B. Lindsley, Frederick W. Miller, Murray H. Passo, Marı́a Pérez-Vázquez, Ann M. Reed, Carol A. Wallace, Patience H. White, Lawrence Zemel, Peter A. Lachenbruch, John R. Hayes, Lisa G. Rider

Bibliographic record

VenueArthritis Care & Research · 2004
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick ChildrenIzaak Walton Killam Health CentreDalhousie University
FundersChina Scholarship Council
KeywordsSupine positionMedicineLift (data mining)Physical medicine and rehabilitationPhysical therapySurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To document and evaluate the scores that normal, healthy children achieve when performing 9 maneuvers of the Childhood Myositis Assessment Scale (CMAS). METHODS: A total of 303 healthy children, 4-9 years of age, were scored as they performed 9 CMAS maneuvers. The data were then evaluated to determine whether normal scores for some maneuvers are age and sex dependent. RESULTS: All children were able to achieve maximum possible scores for the supine to prone, supine to sit, floor sit, floor rise, and chair rise maneuvers. All but 2 4-year-olds achieved a maximum possible score for the arm raise/duration maneuver. Performance of the head lift and sit-up maneuvers varied significantly, depending primarily on age. Children in all age groups had less difficulty performing the leg lift than the head lift or sit-up. CONCLUSION: The normative data generated by this study are of value for interpreting the serial CMAS scores of children with idiopathic inflammatory myopathies.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.015
GPT teacher head0.329
Teacher spread0.314 · 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

Citations49
Published2004
Admission routes1
Has abstractyes

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