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Record W2990831080 · doi:10.4103/ijem.ijem_299_19

A Simple and Reliable Clinical Indicator for Rapid Evaluation of Neuropathy in Busy Diabetes Clinics: “Hair Loss Sign”

2019· article· en· W2990831080 on OpenAlexaboutno aff

Bibliographic record

VenueIndian Journal of Endocrinology and Metabolism · 2019
Typearticle
Languageen
FieldMedicine
TopicSkin Diseases and Diabetes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOrthostatic vital signsPeripheral neuropathyDiabetes mellitusOutpatient clinicMicroneurographyAxon reflexPhysical examinationOrthostatic intolerancePhysical therapyInternal medicineReflexBlood pressureEndocrinology

Abstract

fetched live from OpenAlex

Sir, Diabetes mellitus (DM) currently affects more than 62 million Indians.[1] Studies show that approximately 20% of the patients with diabetes have peripheral neuropathy. Evaluation of diabetic neuropathy is important as it has a bearing on the comprehensive management. Conventional evaluation involves testing the sensory, motor and autonomic functions along with deep tendon reflexes. This is a time consuming process. The huge workload in Diabetes Outpatient clinics in India calls for a simple indicator towards the neuropathic process. One of us (S M Katrak) had observed that the loss of hair on the legs, signifying autonomic neuropathy gives a lead to the extent of somatosensory neuropathic deficits. To test this clinical observation, we designed this study. The study was approved by the Institutional Ethics Committee. Patients with DM reporting paresthesias were included in the study. After obtaining informed valid consent, history was recorded (age, sex, duration of DM, peripheral and autonomic symptoms viz. paresthesias, constipation, urinary complaints, abnormal sweating, and exercise intolerance) and clinical examination was performed (motor system, sensory system viz. fine and crude touch, pain and temperature, vibration and position sense were noted; reflexes and autonomic system viz. resting tachycardia, orthostatic hypotension and the level of hair loss was documented).[2] For the measurement of sensory deficit and hair changes, malleolus was taken as point zero. Toronto Clinical Scoring System (TCSS) was used for grading the severity of neuropathy.[3] In this cohort of 107 patients, mean age was 61.9 years and mean duration of DM was 8.7 years. Twenty four patients had no neuropathy (TCSS score ≤5), 41 had mild neuropathy (TCSS 6-8), 14 had moderate (TCSS 9-11) and 28 patients had severe neuropathy (TCSS ≥12). Of the 83 patients documented to have neuropathy (TCSS score ≥6), 36 (43%) had a distal stocking hair loss which correlated with the level of pain and temperature loss or the level of impaired fine or crude touch [Figure 1]. Of these 36 patients, 11 had moderate and 25 had severe neuropathy. Thus, 79% patients with moderate and 89% with severe neuropathy had hair loss over the distal part of the legs which corresponded with the level of sensory impairment. None of the patients with mild, pure sensory neuropathy exhibited any hair loss.Figure 1: Correlation of level of sensory loss (black line) to absence of hair (hair loss sign)Thus the level of hair loss, when present, can be used as a rapid and reliable clinical indicator of moderate to severe diabetic peripheral neuropathy. The somatic components of sensory neuropathy appear to correlate well with this component of autonomic neuropathy (hair loss). The finding of hair loss and its level on the leg (hair loss sign) can serve the clinician well in his busy diabetes clinic. Further, this could potentially be used in follow-up studies of these individuals with diabetes. The limitations of this study are that patients with mild neuropathy may not exhibit this sign and in female patients who wax their hair, the utility of the sign is lost. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

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)0.002
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 score0.056
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.027
GPT teacher head0.342
Teacher spread0.315 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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