The Need for a Meaningful and Practical Classification of Asthma Severity
Bibliographic record
Abstract
Assessing asthma severity based on symptoms and convenient parameters, such as peak flow rates, is an indispensable method for the management of asthma.Several systems categorizing asthma severity have been developed in the United States of America, the United Kingdom, and Canada, and are routinely used to follow patients with asthma.In this issue of Clinical Medicine & Research, Colice 1 reviews the features, strengths and weaknesses of these systems.When making comparisons, it is difficult to avoid the temptation to seek the best system, although any of the developed classification systems may be as useful as the next.When it comes to practical outcomes, if applied properly and consistently, these systems are valuable tools for the management of asthma and the well being of patients.It is better to have a familiar and tried classification system, even if imperfect, than to have none.As an ancient Greek proverb states, "any measure could be the best one."The critical question in the development of any system to classify asthma severity is not in its applicability or easiness nor is it in the management of symptoms.It is in the optimal interpretation of the results, the ability to prognosticate, and especially the ability to assess the risk for fatal asthma; these are the major shortcomings of all current asthma classification systems.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.052 | 0.073 |
| Insufficient payload (model declined to judge) | 0.005 | 0.009 |
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".