Rising to the GINA Asthma Challenge: thinking beyond just asthma
Bibliographic record
Abstract
We would like to thank Z. Pond and co-workers for their interest in our recent editorial with regard to the Global Initiative for Asthma (GINA) Asthma Challenge We are also delighted by their enthusiasm, in terms of embracing this concept, and framing the issue in the context of their local population. We recognise that the GINA recommendations provide a framework for the management of asthma in the general population of patients [2], but it is also recognised, within the body of the document, that management needs to be individualised. As Z. Pond and co-workers have outlined patient management must go beyond the appropriate prescription of medications, but also take account of the sociocultural factors We also feel that the assessment of the phenotype of asthma patients should take account of these factors as they are likely to contribute to an increased risk of hospitalisation. We strongly recommend that others embrace this approach of adapting locally our global challenge to reduce asthma hospitalisations. We suggest that by using the GINA asthma strategy as a framework for achieving asthma control that we can move closer to achieving our ultimate aim of reducing morbidity and mortality associated with this global public health challenge. It should be noted that our challenge of a 50% reduction in hospitalisations is targeted at a general population level and we appreciate that this may not be feasible in a more specialised setting, such as Z.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.018 | 0.059 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".