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Record W2420287443 · doi:10.1002/mus.25223

Peripheral nerve high‐resolution ultrasound in diabetes

2016· article· en· W2420287443 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMuscle & Nerve · 2016
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsUniversity Health NetworkToronto General HospitalUniversity of TorontoLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity of Calgary
Fundersnot available
KeywordsMedicinePeripheralDiabetes mellitusPeripheral neuropathyUltrasoundPolyneuropathyPeripheral nervePeripheral nervous systemProspective cohort studySurgeryInternal medicineRadiologyAnatomyEndocrinologyCentral nervous system

Abstract

fetched live from OpenAlex

INTRODUCTION: High-resolution ultrasound (HRU) is used in the diagnosis of peripheral neuropathies. There are conflicting data regarding HRU findings in patients with diabetic sensorimotor polyneuropathy (DSP). Our purpose in this study was to measure nerve cross-sectional areas (CSAs) in patients with diabetes, with and without DSP. METHODS: We performed a prospective peripheral nerve HRU study of 100 diabetic subjects, assessed the CSA at predefined sites, and compared the results with those of 100 normal subjects. We evaluated the use of individual CSA values and various summary scores for diagnosis of DSP. RESULTS: Diabetic subjects had higher CSA values than healthy volunteers, and those with DSP had higher CSA values. Three or more enlarged CSA sites predicted DSP with 64% sensitivity and 77% specificity. CONCLUSIONS: Peripheral nerves are enlarged diffusely in diabetic patients, including sites not susceptible to bony compression. The number of enlarged CSA values can help predict the presence of DSP. Muscle Nerve, 2016 Muscle Nerve 55: 171-178, 2017.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score1.000

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)0.0010.000

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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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