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Record W3119565519 · doi:10.1002/mus.27170

Determinants and functional impacts of diaphragmatic involvement in patients with inclusion body myositis

2021· article· en· W3119565519 on OpenAlexaff
Marie‐Hélène Lelièvre, Marie Hudson, Stéphan A. Botez, Bruno‐Pierre Dubé

Bibliographic record

VenueMuscle & Nerve · 2021
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsJewish General HospitalMcGill UniversityCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsInclusion body myositisMedicineDiaphragm (acoustics)Vital capacityInternal medicinePhysical therapyCardiologyLungMyositisSurgeryPhysical medicine and rehabilitationLung functionDiffusing capacity

Abstract

fetched live from OpenAlex

BACKGROUND: We evaluated the functional consequences of diaphragm involvement in patients with inclusion body myositis (IBM). METHODS: Ultrasound diaphragm thickening fraction (TFdi), lung function and dyspnea levels were compared between IBM patients and matched controls. Patients with IBM were grouped into "low" and "high" diaphragm activity based on TFdi values (with cutoff value being the lowest observed TFdi in the control group), and clinical characteristics were compared between groups. RESULTS: 20 IBM patients were included. TFdi was significantly lower in patients and correlated with time since symptom onset (rho = 0.74, P < .001). Patients had significantly lower forced vital capacity and higher dyspnea scores than controls. IBM patients with "low" diaphragm activity (n = 9) had lower 6-min walking distance, higher resting and exertional dyspnea and a larger positional decrease in vital capacity (all P ≤ .03) than patients with 'high' activity. Timed Up and Go time and St. George's Respiratory Questionnaire were not different between groups. CONCLUSIONS: Diaphragm involvement in IBM is related to disease duration and has detrimental effects on lung function, dyspnea and exercise capacity. Further studies are required to investigate its potential as a therapeutic target.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.396

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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 teacher head, 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

Citations14
Published2021
Admission routes1
Has abstractyes

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