Individualized prophylaxis for optimizing hemophilia care: can we apply this to both developed and developing nations?
Bibliographic record
Abstract
Prophylaxis is considered optimal care for hemophilia patients to prevent bleeding and to preserve joint function thereby improving quality of life (QoL). The evidence for prophylaxis is irrefutable and is the standard of care in developed nations. Prophylaxis can be further individualized to improve outcomes and cost effectiveness. Individualization is best accomplished taking into account the bleeding phenotype, physical activity/lifestyle, joint status, and pharmacokinetic handling of specific clotting factor concentrates, all of which vary among individuals. Patient acceptance should also be considered. Assessment tools (e.g. joint status imaging and function studies/scores, QoL) for determining and monitoring risk factors and outcome, as well as population PK profiling have been developed to assist the individualization process. The determinants of optimal prophylaxis include (1) factor dose/dosing frequency, hence, cost/affordability (2) bleeding triggers (physical activity/lifestyle, chronic arthropathy and synovitis) and (3) bleeding rates. Altering one determinant results in adjustment of the other two. Thus, the trough level to protect from spontaneous bleeding can be increased in patients who have greater bleeding risks; and prophylaxis to achieve zero joint bleeds is achievable through optimal individualization. Prophylaxis in economically constrained nations is limited by the ill-affordability of clotting factor concentrates. However, at least 5 studies on children and adults from Thailand, China and India have shown superiority of low dose (~5–10 IU kg −1 2-3× per week) prophylaxis over episodic treatment in terms of bleed reduction, and quality of life, with improved physical activity, independent functioning, school attendance and community participation. In these nations, the prophylaxis goals should be for improved QoL rather than “zero bleeds” and perfect joints. Prophylaxis can still be individualized to affordability. Higher protective trough level can be achieved by using smaller doses given more frequently without an increase in consumption/cost. The bleeding trigger can also be down-regulated by avoiding unnecessary injury, and by engaging in judicious strengthening exercises appropriate to the joint status to improve balance and joint stabilization. Central to the success of prophylaxis are clinics with comprehensive care that provide the necessary professional expertise, support, and counseling, to educate patients, families, and other healthcare professionals, and to support research for improved hemophilia care.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".