Speaker Abstracts
Bibliographic record
Abstract
Introduction: Throughout life span patient with haemophilia and the comprehensive care team are confronted with many challenges and decisions.The multidisciplinary session will focus and discuss different tasks around delivery, childhood, adolescence and the early and late adulthood.In the comprehensive care haemophilia nurses have a pivotal role in supporting families and patients to enable them in self-management.Method: The following phases in the life span of haemophilia patient will investigate: learning selfinfusion, achieving self-management in adolescence, adherence issues and coping with haemophilia.A review of the literature available and a best practice analyses will be performed to support the decision making of health care providers for patients with haemophilia.Results: Patients with haemophilia have different challenges in life.Nearly all severe haemophilia patients practise self-infusion.Learning self-infusion requires time and effort.During adolescence, patients experience physiological, cognitive and psycho-social challenges.Adolescents are struggling with 'normal' developments, but patients with haemophilia also need to achieve selfmanagement skills over their own treatment.Selfmanagement skills are essential to perform and maintain prophylaxis throughout life.In addition, for effective prevention of bleeding, high adherence to treatment is crucial.A single bleed can lead to irreversible damage, especially in a joint or the central nervous system.Furthermore, in spite of adequate treatment, many patients with haemophilia are concerned about the occurrence of a bleed or elderly patients have to deal with the consequences of joint-damage.The way patients with haemophilia deal with disease-related stress, largely depends on their coping strategies.It is important to define patients with inadequate coping behaviour to offer individuals support.Conclusion: Patients with haemophilia are facing many extra challenges in life than healthy people.The comprehensive team can support them with proper care through these challenges.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.806 | 0.641 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".