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Record W2782222724 · doi:10.15557/jou.2017.0036

High resolution ultrasonography of the tibial nerve in diabetic peripheral neuropathy

2017· article· en· W2782222724 on OpenAlexaboutno aff
Kunwarpal Singh, Kamlesh Gupta, Sukhdeep Kaur

Bibliographic record

VenueJournal of Ultrasonography · 2017
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeripheral neuropathyDiabetic neuropathyTibial nerveDiabetes mellitusPeripheralSurgeryInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

AIM OF THE STUDY: High-resolution ultrasonography of the tibial nerve is a fast and non invasive tool for diagnosis of diabetic peripheral neuropathy. Our study was aimed at finding out the correlation of the cross sectional area and maximum thickness of nerve fascicles of the tibial nerve with the presence and severity of diabetic peripheral neuropathy. MATERIAL AND METHODS: 75 patients with type 2 diabetes mellitus clinically diagnosed with diabetic peripheral neuropathy were analysed, and the severity of neuropathy was determined using the Toronto Clinical Neuropathy Score. 58 diabetic patients with no clinical suspicion of diabetic peripheral neuropathy and 75 healthy non-diabetic subjects were taken as controls. The cross sectional area and maximum thickness of nerve fascicles of the tibial nerves were calculated 3 cm cranial to the medial malleolus in both lower limbs. RESULTS: and 0.30 mm respectively). CONCLUSION: The cross sectional area and maximum thickness of nerve fascicles of the tibial nerve is larger in diabetic patients with or without peripheral neuropathy than in healthy control subjects, and ultrasonography can be used as a good screening tool in these patients.

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 score0.037
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.243
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

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

Citations62
Published2017
Admission routes1
Has abstractyes

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