Evaluation of asthma control using Global Initiative for Asthma criteria and the Asthma Control Test in Uganda
Bibliographic record
Abstract
SETTING: Chest clinic of a national referral hospital in a resource-limited country. OBJECTIVES: To determine the level of asthma control, factors influencing asthma control and the accuracy of the Asthma Control Test (ACT). DESIGN: We collected demographic and clinical data and administered the Global Initiative for Asthma (GINA) criteria test and the ACT. The proportions of patients in each of the GINA and ACT control categories (uncontrolled, partly controlled and well controlled) were calculated. Multivariate analysis was performed to identify factors associated with asthma control. Diagnostic test parameters for the ACT using GINA criteria as gold standard were calculated. RESULTS: Of 88 asthma patients enrolled, 67% were female. The median age was 34 years (range 12-85). Using GINA criteria, respectively 59 (67%), 17 (19%) and 12 (14%) patients had uncontrolled, partly controlled and well controlled asthma; per ACT, the corresponding figures were respectively 40% (35/88), 43% (38/88) and 17% (15/88). ACT sensitivity, specificity, positive predictive and negative predictive value were respectively 95%, 92%, 99% and 73%. Nasal congestion was associated with uncontrolled asthma (P = 0.031). CONCLUSION: The majority of the patients at the Mulago Hospital have inadequately controlled asthma, and this is associated with nasal congestion. A simple symptom questionnaire, the ACT, can correctly classify asthma control.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".