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Record W2013334395 · doi:10.1136/bjo.2003.037317

Exaggerated relative nasal-temporal asymmetry of macular capillary blood flow in patients with clinically significant diabetic macular oedema

2005· article· en· W2013334395 on OpenAlexafffund
Chris Hudson

Bibliographic record

VenueBritish Journal of Ophthalmology · 2005
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooDiabetes UK
KeywordsMedicineBlood flowOphthalmologyDiabetes mellitusCapillary actionDiabetic retinopathyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

AIMS: To compare the macular capillary blood flow of patients with clinically significant diabetic macular oedema (DMO) with that of non-diabetic subjects and to determine the relation between blood flow and capillary leakage in patients with DMO. METHODS: The sample comprised 45 non-diabetic subjects (mean age 59 years) and 18 type 2 patients with clinically significant DMO (mean age 60 years). Macular capillary blood flow measurements were acquired using the Heidelberg retina flowmeter (HRF) and a 10 degrees x2.5 degrees scan field centred on the fovea. Fluorescein angiography was undertaken on each of the diabetic patients after the completion of HRF measurements. RESULTS: Temporal macular capillary blood flow was significantly lower for the patients with clinically significant DMO compared with age matched non-diabetic subjects (ANCOVA, p = 0.0011) while relative nasal-temporal asymmetry of macular capillary blood flow was significantly higher (p = 0.0125). Nasal-temporal asymmetry of macular capillary blood flow was significantly higher for the patients with DMO and capillary leakage within the scan area (two tailed t test, p = 0.0071). Macular capillary blood flow was always lower in areas of DMO and capillary leakage. CONCLUSION: Capillary blood flow was reduced in areas of DMO and capillary leakage, suggesting the presence of a localised perturbation of capillary blood flow regulation.

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 score0.034
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.266
Teacher spread0.256 · 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

Citations16
Published2005
Admission routes2
Has abstractyes

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