CoDysAn: A Telemedicine Tool to Improve Awareness and Diagnosis for Patients With Congenital Dyserythropoietic Anemia
Bibliographic record
Abstract
Congenital Dyserythropoietic Anemia (CDA) is a heterogeneous group of hematological disorders characterized by chronic hyporegenerative anemia and distinct morphological abnormalities of erythroid precursors in the bone marrow. In many cases, a final diagnosis is not achieved due to different levels of awareness for the diagnosis of CDAs and lack of use of advanced diagnostic procedures. Researchers have identified five major types of CDA: types I, II, III, IV and X-linked dyserythropoietic anemia and thrombocytopenia (XLDAT). Proper management in CDA is still unsatisfactory, as the different subtypes of CDA have different genetic causes and different but overlapping patterns of signs and symptoms. For this reason, we developed a new telemedicine tool that will help doctors to achieve a faster diagnostic for this disease. Using open access code, we have created a responsive webpage named CoDysAn (Congenital Dyserythropoietic Anemia) that includes practical information for CDA awareness and a step-by-step diagnostic tool based on a CDA algorithm. The site is currently available in four languages (Catalan, Spanish, Italian and English). This telemedicine webpage is available at http://www.codysan.eu
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.009 |
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".