Haemophilia at various stages of life: design of new therapeutic strategies through an interactive course--the Kogeniale project.
Bibliographic record
Abstract
BACKGROUND: High-quality evidence is lacking in several areas of haemophilia treatment, in part because little time is allocated to the treatment and care of haemophilia in university education in Italy. Physicians caring for patients with haemophilia must, therefore, rely on their information on background pathophysiology and more experienced colleagues. This makes diagnostic and therapeutic choices difficult, especially when the patient has concomitant disorders or psychological issues. MATERIAL AND METHODS: This article describes a course to educate young physicians who were already engaged in the management of haemophilia on the emerging and unmet issues of haemophilia care and to implement existing guidelines. Physicians (n=53) already caring for patients with haemophilia in their haematology, internal medicine, or paediatric practices in Italy attended the course. Problem-solving group activity and open discussion were the methods chosen to formulate consensus statements. During the specifically designed interactive course, three clinical cases were simulated: a young child with congenital dislocation of the hip, an adolescent refusing prophylactic treatment, and an elderly man with cardiovascular disorders. The physicians were asked questions during the course and, through a Wi-Fi console, were able to answer and discuss each case interactively. RESULTS: Following discussion of each case, agreement was reached regarding general statements on the management of patients with severe haemophilia A in the three different age ranges considered. DISCUSSION: This project helped to outline useful decision-making tools for handling diagnostic and treatment issues in the field of haemophilia.
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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".