Definitions for haemophilia prophylaxis and its outcomes: The Canadian Consensus Study
Bibliographic record
Abstract
The creation of acceptable standard definitions for terms used in the care and assessment of haemophilia patients has become increasingly important, as a growing number of international clinical studies have been initiated. The Delphi approach has been used in health research to reach consensus in large groups and can be used to develop definitions by using several iterations of surveys eliciting opinions from specialists in the field. Three consecutive surveys were designed based on the Delphi approach and distributed to specialist physicians, nurses and physiotherapists in order to develop definitions for seven haemophilia terms: 'primary prophylaxis', 'secondary prophylaxis', 'target joint', 'joint bleed', 'significant soft-tissue bleed', 'superficial soft-tissue bleed' and 'mucosal bleed'. Suggestions were solicited, compiled into a subsequent survey and fed back to the group to rank-order the importance of each suggested component of the definition. Final definitions were created using the top-ranked suggestions and sent back to the experts for approval. Five of the seven terms were highly endorsed with greater than 90% agreement. Some differences in agreement were found when analysed by profession. Haemophilia terms were successfully defined using the Delphi approach. Further refinement from members of the international haemophilia community will ensure that comprehensive standard definitions can be used in multicentre studies in the future.
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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 | 0.117 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".