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Record W4280633396 · doi:10.1097/cpt.0000000000000207

The Association of the Medical Research Council Scale and Quantitative Computerized Dynamometry in Patients After Critical Illness: An Exploratory Study

2022· article· en· W4280633396 on OpenAlexaff
Anna Michalski, Leanna Souza-Barros, Priscila Robles, Jill Cameron, Margaret S. Herridge, Sunita Mathur

Bibliographic record

VenueCardiopulmonary Physical Therapy Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsToronto General HospitalQueen's UniversitySt Joseph's Health CentreUniversity of Toronto
Fundersnot available
KeywordsIsometric exerciseInterquartile rangeMedicinePhysical therapyMuscle weaknessPhysical medicine and rehabilitationAnkleInternal medicineSurgery

Abstract

fetched live from OpenAlex

Purpose: The purpose of this study was to determine the association between isometric peak torque obtained using the Biodex dynamometer (gold standard) to the Medical Research Council (MRC) scale for knee extension (KE), ankle dorsiflexion (DF), and shoulder flexion (SF) in patients after intensive care unit (ICU) discharge. Methods: A cross-sectional observational substudy was conducted. Adults with at least 7 days of mechanical ventilation were recruited from a parent study of outcomes after critical illness. Muscle strength of KE, DF, and SF was measured using the MRC scale (grades 0–5) and the Biodex dynamometer (peak torque, % predicted) at 3 months post-ICU discharge. Results: Twenty participants were included in the study. The median and interquartile ranges for MRC scores for KE, DF, and SF were 4.5 (IQR = 1), 4.5 (IQR = 1), 4.0 (IQR = 1), respectively. No significant correlations were found between isometric peak torque and MRC scores for any muscle group at 3 months post-ICU discharge. Conclusion: Medical Research Council scale may be limited in the ability to detect muscle weakness post-ICU discharge. Quantitative dynamometry is recommended in the post-ICU period to identify individuals with muscle weakness who may benefit from physical rehabilitation.

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)0.006
metaresearch head score (Gemma)0.006
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 score0.066
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.046
GPT teacher head0.343
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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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