The existing scenario of haemophilia care in Canada and China - A review
Bibliographic record
Abstract
Hemophilia is an X-linked recessive genetic disorder which affects approximately 400,000 people globally. Differing healthcare reimbursement systems, budgetary constraints and geographical and cultural factors make it difficult for any country to fully deliver ideal care. Although developed countries have sufficient treatment products available, they are burdened by the higher expectation of outcomes, coupled with insufficient supportive care to monitor adherence and outcomes and to implement regular follow-up. In contrast, developing regions may not have ready access to factor replacement, but have developed excellent physiotherapy and rehabilitation programs. Although there are multiple studies that have attempted to assess country-specific variations in hemophilia care, very few compare hemophilia care between economically unequal countries and the challenges in achieving optimal hemophilia care. This literature review tries to bridge this gap and throws light on the country-specific differences in epidemiology, standard of hemophilia care and challenges faced in Canada and China. Data sources resulted in 20 studies (11 from Canada and 9 from China), which were reviewed. In a developed country, the main advantages are: the early treatment of bleeding episodes and the presence of a specialized interdisciplinary and comprehensive treatment concept. This is not the case in most developing countries, where the government does not have the resources to buy the necessary quantities of coagulation factors in the face of more urgent health priorities and hardly a few patients can afford to pay for their own treatment, even the on-demand home therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".