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Record W4282946629 · doi:10.1097/pep.0000000000000913

Characterizing Pain Among Adolescents and Young Adults With Arthrogryposis Multiplex Congenita

2022· article· en· W4282946629 on OpenAlexaboutno aff
Jaclyn Megan Sions, Maureen Donohoe, Emma Haldane Beisheim, Ryan T. Pohlig, Tracy M. Shank, L. Reid Nichols

Bibliographic record

VenuePediatric Physical Therapy · 2022
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsArthrogryposis multiplex congenitaYoung adultMedicineMcGill Pain QuestionnairePhysical therapyChronic painPain catastrophizingArthrogryposisGerontologyVisual analogue scaleSurgery

Abstract

fetched live from OpenAlex

PURPOSE: Primary study objectives were to ( a ) characterize pain and explore differences between adolescents and adults with arthrogryposis multiplex congenita (AMC) and ( b ) evaluate associations between pain-related outcomes and mobility. METHODS: People who can walk and with AMC completed pain-related questionnaires. RESULTS: Sixty-three participants (28 adolescents and 35 young adults) were recruited. Pain was reported in the past week by 81% of participants; intensity ratings were similar between age groups. Per the McGill Pain Questionnaire, pain severity was significantly lower among adolescents. Adults had a greater number of painful regions compared with adolescents. Greater 7-day average pain intensity, McGill Pain Questionnaire scores, and number of painful regions were associated with reduced functional mobility. CONCLUSIONS: As most adolescents and young adults with AMC have at least mild pain, and pain is associated with mobility, future longitudinal investigations of pain and its functional consequences are warranted.

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)0.002
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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