2010 International consensus algorithm for the diagnosis, therapy and management of hereditary angioedema
Bibliographic record
Abstract
BACKGROUND: We published the Canadian 2003 International Consensus Algorithm for the Diagnosis, Therapy, and Management of Hereditary Angioedema (HAE; C1 inhibitor [C1-INH] deficiency) and updated this as Hereditary angioedema: a current state-of-the-art review: Canadian Hungarian 2007 International Consensus Algorithm for the Diagnosis, Therapy, and Management of Hereditary Angioedema. OBJECTIVE: To update the International Consensus Algorithm for the Diagnosis, Therapy and Management of Hereditary Angioedema (circa 2010). METHODS: The Canadian Hereditary Angioedema Network (CHAEN)/Réseau Canadien d'angioédème héréditaire (RCAH) http://www.haecanada.com and cosponsors University of Calgary and the Canadian Society of Allergy and Clinical Immunology (with an unrestricted educational grant from CSL Behring) held our third Conference May 15th to 16th, 2010 in Toronto Canada to update our consensus approach. The Consensus document was reviewed at the meeting and then circulated for review. RESULTS: This manuscript is the 2010 International Consensus Algorithm for the Diagnosis, Therapy and Management of Hereditary Angioedema that resulted from that conference. CONCLUSIONS: Consensus approach is only an interim guide to a complex disorder such as HAE and should be replaced as soon as possible with large phase III and IV clinical trials, meta analyses, and using data base registry validation of approaches including quality of life and cost benefit analyses, followed by large head-to-head clinical trials and then evidence-based guidelines and standards for HAE disease management.
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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 | 0.046 | 0.085 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".