Target plasma factor levels for personalized treatment in haemophilia: a Delphi consensus statement
Bibliographic record
Abstract
BACKGROUND: Prophylactic replacement with factor concentrate is the optimal treatment for persons with severe haemophilia to avoid or minimize bleeding. This ultimately prevents or reduces joint disease and improves life expectancy and quality of life towards values matching those in the normal population. However, uncertainty still exists around the optimal regimens to be prescribed for prophylaxis. An increasing number of treating physicians and patients are showing interest in patient-tailored approaches to prophylaxis, which aim to harmonize the prophylaxis regimen with the patients' bleeding phenotype, levels of physical activity and a variety of other variables. METHODS: A modified Delphi technique was adopted to generate consensus. The expert panel met in person to set the objectives, be trained on the Delphi technique and agree on the desired level of consensus. Three iterations were used to identify the targets, the scenarios and their combinations. RESULTS: Twenty-eight scenarios and eight target levels were identified and used to issue recommendations. The panel reached the desired level of consensus on positive or negative recommendations. Areas where consensus was not reached were identified and proposed as areas for future research. Prospective assessment of the validity of most of the proposed targets is recommended. CONCLUSIONS: We have generated, by expert consensus, target plasma levels of factor concentrate to be used to tailor treatment for persons with haemophilia.
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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 | 0.184 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".