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Record W2013589036 · doi:10.4239/wjd.v4.i4.135

Association of comorbidities with increasing severity of peripheral neuropathy in diabetes mellitus

2013· article· en· W2013589036 on OpenAlexaboutno aff
Shafina Sachedina

Bibliographic record

VenueWorld Journal of Diabetes · 2013
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiabetes mellitusInternal medicinePeripheral neuropathyEtiologyType 2 diabetesPopulationType 2 Diabetes MellitusProspective cohort studyComorbidityCohortGastroenterologyEndocrinology

Abstract

fetched live from OpenAlex

AIM: To analyze a large population of patients with diabetes and peripheral neuropathy (PN) to determine other meaningful comorbid etiologies for PN. METHODS: Peripheral Neuropathy is a common complication of type 1 and 2 diabetes mellitus; however, other potential causes for PN may be co-existing in patients with diabetes. A prospective cohort study was performed to assess patients with diabetes and PN. We compared patients having PN due solely to diabetes with patients possessing co-existing comorbidities, performing clinical (Toronto Clinical Scoring System and the Utah Early Neuropathy Scale), laboratory and electrophysiological assessments in all patients. RESULTS: Patients with either type 1 or 2 diabetes mellitus and co-existing comorbidities did not have more severe clinical or electrophysiological PN phenotypes overall. However, in patients with type 1 diabetes, presence of a lipid disorder was associated with greater PN severity. In type 2 diabetes patients, both a lipid disorder and cobalamin deficiency were associated with greater PN severity. There was no additive effect upon PN severity with presence of three or more comorbid etiologies. CONCLUSION: The presence of specific, and not general, comorbidities in patients with type 1 or 2 diabetes corresponds with greater PN severity.

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)0.001
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 score0.024
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.218
Teacher spread0.210 · 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

Citations18
Published2013
Admission routes1
Has abstractyes

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