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Record W2950594102 · doi:10.1038/s41598-019-45116-z

Early corneal nerve fibre damage and increased Langerhans cell density in children with type 1 diabetes mellitus

2019· article· en· W2950594102 on OpenAlexafffund
Maryam Ferdousi, Kenneth Romanchuk, Jean K. Mah, Heidi Virtanen, Christine Millar, Rayaz A. Malik, Danièle Pacaud

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersAlberta Children's Hospital Research InstituteJuvenile Diabetes Research Foundation CanadaJuvenile Diabetes Research Foundation International
KeywordsMedicineLangerhans cellType 2 Diabetes MellitusNerve fibreDiabetes mellitusType 1 diabetesOphthalmologyEndocrinologyAnatomyImmunologyAntigen

Abstract

fetched live from OpenAlex

Corneal confocal microscopy (CCM) has been used to identify corneal nerve damage and increased Langerhans cell (LC) density in adults with Type 1 diabetes mellitus (T1DM). The purpose of this study was to evaluate whether corneal confocal microscopy can identify early corneal nerve damage and change in LC density in children and adolescents with T1DM. 64 participants with T1DM (age-14.6 ± 2.5 years, duration of diabetes-9.1 ± 2.7 years, HbA1c-75.66 ± 2.53 mmol/mol [9.1 ± 1.8%]) and 48 age-matched healthy control subjects underwent CCM. Sub-basal corneal nerve morphology and the density of mature and immature LCs was quantified. Corneal nerve fibre length and branch density were lower, whilst fibre density and tortuosity did not differ and both immature and mature LC density was significantly higher in T1DM compared to control subjects. There was no association between HbA1c and duration of diabetes with nerve fibre parameters or LC's density. Children and adolescents with T1DM demonstrate early immune activation and nerve degeneration.

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 score0.006
Threshold uncertainty score0.382

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)0.0000.000

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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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

Citations64
Published2019
Admission routes2
Has abstractyes

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