Bibliographic record
Abstract
The 2010 International Consensus Algorithm for the Diagnosis, Therapy and Management of Hereditary Angioedema was arrived at during the Canadian Hereditary Angioedema Network (CHAEN)/Réseau Canadien d'angioédème héréditaire (RCAH) second meeting held May 15 th /16 th , 2010, Toronto, Canada and was cosponsored by CHAEN/RCAH, the Canadian Society of Allergy and Clinical Immunology, and the University of Calgary and was funded through an unrestricted educational grant from CSL Behring.This is the third international consensus and is meant to be a living document requiring continual updating and rethinking.The first consensus conference was scheduled for Toronto, Ontario, Canada in April 2003 but was SARSed out.That conference was rescheduled and held in Toronto in October 2003 and published in 2004.The next consensus was again held in Toronto Canada in 2006 and rediscussed in Budapest in 2007 and published in 2008.This third consensus conference was in danger of being ashed out from the volcanic activity in Iceland making planning of such meetings a challenge.Rare disorders such as Hereditary Angioedema require international collaboration to push ahead with progress in the management of the disorders.The Hungarian group under Dr. Henriette Farkas and the Italian group under Dr. Marco Cicardi have certainly led the way in organizing these essential get-togethers.Patient Group participation in these discussions has been strongly encouraged and the Consensus Algorithms have been signed off by various National Patient Organizations.The patients should decide how they wish to be treated.I usually bore audiences with my motto: It can be done -It must be done for the sake of our patients.This concept continues in this third consensus algorithm development.We have moved from 2003 from only a few controlled trials in prophylaxis and treatment in HAE-Types I and II to now several clinical trials in various stages of publication.Prophylaxis options have moved from anti-fibrinolytics and androgens
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".