Management of haemophilia patients in the COVID‐19 pandemic: Experience in Wuhan and Tianjin, two differently affected cities in China
Bibliographic record
Abstract
OBJECTIVE: To identify lessons learned from haemophilia care experience in Wuhan (COVID-19 outbreak epicenter in China) and Tianjin (with relatively low COVID-19 incidence) in the pandemic. METHODS: We compared the challenges in haemophilia management attributed to local COVID-19 containment policies, healthcare resource availability, clotting factors supply, daily living restrictions and coping strategies employed. RESULTS: Wuhan was in lockdown with strict traffic controls, enforced quarantine and overwhelmed resources. Tianjin was in relatively relaxed countermeasures to COVID-19. In Wuhan, haemophilia treatment (for bleeding, prophylaxis, multidisciplinary team care, immune tolerance induction) and patient education were severely affected, while the challenges in Tianjin were less. In both cities, patients' fear for COVID-19 infection also affected their management. Coping strategy in Wuhan included channelling of clotting factors supply from hospitals to nine pharmacies; timely transfers of in-need patients to healthcare facilities by a volunteer service network jointly coordinated by the government, hospitals and the community. Although factor concentrate supply in each city was adequate, patients still worried whether there would be enough supply to last through the pandemics. Consequently, many downgraded their treatment regimens resulting in increased bleeding episodes. In both cities, telemedicine was promoted for patient care and education. CONCLUSIONS: The COVID-19 pandemic had varying adverse impacts on haemophilia care depending on the local infection incidence. Our experience suggests that haemophilia management strategies in the pandemic need to be established according to the local virus containment/mitigation policies, daily living restrictions and resource availability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".