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Waardenburg syndrome: About three cases at the Thies Regional Hospital Center

2025· article· en· W4409988874 on OpenAlexaff
GBP Aloumba, MB Badji, N Ndiaye, Rachid Vitamine, TJ Elongo, BM Diatéwa, H. Kane, E. Ba

Bibliographic record

VenueJournal of African clinical cases and reviews. · 2025
Typearticle
Languageen
FieldMedicine
TopicMedicine and Dermatology Studies History
Canadian institutionsCentre Casa
Fundersnot available
KeywordsCenter (category theory)Computer scienceMedicineOptometry

Abstract

fetched live from OpenAlex

Iris heterochromia is a congenital anomaly of iris colouration that occurs rarely. Whatever its intensity or laterality, it is often indicative of a malformative syndrome. The aim of this study was to investigate the epidemiological, clinical and therapeutic features of Waardenburg syndrome in the ophthalmology department of the Thies regional hospital. We report three cases of iris heterochromia including this syndrome diagnosed over a period of 4 years. Irial heterochromia was the main reason for consultation in the two girls, while the boy was consulted for sickle cell retinopathy. Auditory evoked potentials were carried out in all the children, revealing bilateral sensorineural hearing loss, thus making it possible to establish the association between congenital iris heterochromia, sensorineural hearing loss and Waardenburg syndrome. The diagnostic criteria of the Waardenburg consortium were used to classify the first and third patients as type 1 and the second patient as type 4. The absence of diagnostic certainty due to the contribution of genetics should never lead to a misdiagnosis. Ultimately, congenital iris heterochromia is a reality; however, its unilateral manifestation is rare. Ideally, clinicians should look for associated anomalies, as this heterochromia could conceal a malformative syndrome, as the management of these patients requires multidisciplinary consultation. Keywords: syndrome, Waardenburg, heterochromia, hearing loss, Thies.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.078
GPT teacher head0.386
Teacher spread0.309 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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