Bibliographic record
Abstract
The World Federation of Haemophilia (WFH) estimates that worldwide, approximately 70% of patients with hemophilia are underdiagnosed and untreated. Most live in developing countries. Although in developed countries the life expectancy of hemophiliacs is close to that of healthy persons, this is not the case in developing countries. Great disparity also exists in the treatment of hemophiliacs, especially when this relates to available factor concentrates. There are many reasons for the inadequate care of hemophilic patients: the perception of rarity of the disease; lack of laboratory facilities to diagnose the disorder; lack of understanding of the disorder by patients, their relatives, and even healthcare providers; poorly developed blood bank facilities; and lack of adequate factor supply are just some examples. The WFH attempts to address many of these issues by establishing hemophilia care programs and by educating and training healthcare practitioners so that a healthcare team can be organized that attempts to ameliorate these problems and provides treatment options. In the last few years, a considerable number of developing countries have been organized to deliver at least a minimum of care, and attempts have been made to obtain support from appropriate governmental sources.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".