MétaCan
Menu
Back to cohort
Record W2035526136 · doi:10.1097/phm.0b013e31822415b6

Assessing Weakness in Patients with Ulnar Neuropathy

2011· article· en· W2035526136 on OpenAlexafffund
Matti D. Allen, Timothy J. Doherty

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2011
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsUlnar neuropathyMedicineElbowUlnar nerveWeaknessDorsumEntrapment NeuropathySurgeryAnatomyCarpal tunnel syndrome

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to compare the use of a custom intrinsic hand dynamometer (HD) with that of a standard pinch dynamometer (PD) in assessing strength in patients with ulnar neuropathy at the elbow. DESIGN: Nine patients (age, 53 ± 3 yrs) with clinical and electrophysiological features of ulnar neuropathy at the elbow with conduction block (CB) were studied. All underwent bilateral ulnar motor nerve conduction studies recording from the first dorsal interosseous and a quantitative measurement of strength of the first dorsal interosseous using a custom-made HD and a standard PD. RESULTS: The maximal strength of the ulnar neuropathy at the elbow-affected side (16.2 ± 8.0 N) was found to be significantly lower than that of the unaffected side (27.9 ± 11.2 N), as measured by HD. Strength differences were not significant between the affected (62.7 ± 26.4 N) and unaffected sides (48.0 ± 20.5 N) using PD. HD force decrement (in comparison with the unaffected limb) correlated strongly with CB percentage (r = 0.74). No relationship was found between PD and CB (r = 0.05). CONCLUSIONS: HD was better able to measure the weakness of affected muscles than did PD, and its results correlated well with the extent of electrophysiological CB. Therefore, a custom HD would provide a better indication of disease severity, progression, or improvement in strength in studies of ulnar neuropathy at the elbow with CB.

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)0.001
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 score0.385
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.280
Teacher spread0.270 · 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

Citations8
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of Physical Medicine & RehabilitationSame topicPeripheral Nerve DisordersFrench-language works237,207