From ignorance to denial about an orphan, but no rare, genetic disease: Ehlers-Danlos Syndrome (EDS type III)
Bibliographic record
Abstract
The development of medicine is increasingly based on biology and medical imaging keeps the doctor away from the bedside. In addition, both doctors and patients believe less and less in clinical medicine. This is compounded by poor application of evidence-based medicine and leads to give probative value to biological tests and, in particular, genetic tests. One can add the fragmentation of medical practice in specialties and subspecialties which hampers a comprehensive view of the patient. This and some medical prejudices, including a lack of confidence in the patient, explain that a poorly described disease, with multiple manifestations, without biological or radiological test, has very little chance of being recognized. This is the case of a multi painful hemorrhagic hypermobile syndrome with asthenia, called Ehlers-Danlos syndrome (EDS) which, despite its relative frequency, continues to be ignored by almost all of the medical profession. This leads to a delay in diagnosis of about 21 years for women and 15 years for men, after the onset of symptoms, and leaves in ignorance people with a genetically transmitted disease. Our approach relies on clinical observation of 612 cases that we have personally received in consultation at the hospital.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".