MARFAN SYNDROME IN A GHANAIAN MALE: THE DIAGNOSTIC CHALLENGES
Bibliographic record
Abstract
INTRODUCTION Marfan syndrome (MFS) is an inherited connective tissue disease that occurs following an autosomal dominant gene mutation in the fibrillin-1gene (FBN1) 1. The protein produced by this mutated gene is an essential component of most connective tissue and being structurally abnormal, results in a wide range of specific ophthalmological, skeletal, and cardiovascular abnormalities that characterize MFS 1. The disease was discovered when Antoine - Bernard Marfan diagnosed a 5-year-old named Gabrielle who presented with skeletal signs 2. Current studies estimate the prevalence of MFS at 6.5/100,00 3. Experts, in 1986, at Berlin created the first clinical criteria for diagnosing MFS known as the Berlin Nosology 2. A new criterion was detailed in 1996 (Ghent I criteria) on account of high false positive results. In 2010 the Ghent 1 criterion was modified to include specifically FBNI mutation, aortic root dilatation, and ectopic lentis as the mainstay of MFS diagnosis (Ghent II). The formulation of this nosology was essential for the avoidance of inconclusive diagnosis and differentiation from conditions presenting with similar manifestations 1, 2. Clinical manifestations of this disorder include cardiovascular, ophthalmic, musculoskeletal, craniofacial, and cutaneous abnormalities 4. Amongst the cardiovascular manifestations, aortic dilatation and mitral regurgitation from mitral valve prolapse occur frequently5, 6.In this case report we describe the incidental finding of a young African male with classic Marfan’s syndrome but initially diagnosed at the age of 23years. We further explore the barriers to early diagnosis in our part of the world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".