Development of a conceptual model of childhood asthma to inform asthma prevention policies
Bibliographic record
Abstract
BACKGROUND: There is no definitive cure for asthma, as prevention remains a major goal. Decision analytic models are routinely used to evaluate the value-for-money proposition of interventions. Following best practice standards in decision-analytic modelling, the objective of this study was to solicit expert opinion to develop a concept map for a policy model for primary prevention of asthma. METHODS: We reviewed currently available decision analytic models for asthma prevention. A steering committee of economic modellers, allergists and respirologists was then convened to draft a conceptual model of paediatric asthma. A modified Delphi method was followed to define the context of the problem at hand (evaluation of asthma prevention strategies) and develop the concept map of the model. RESULTS: Consensus was achieved after three rounds of discussions, followed by concealed voting. In the final conceptual model, asthma diagnosis was based on three domains of lung function, atopy and their symptoms. The panel recommended several markers for each domain. These domains were in turn affected by several risk factors. The panel clustered all risk factors under three groups of 'patient characteristic', 'family history' and 'environmental factors'. To be capable of modelling the interplay among risk factors, the panel recommended the use of microsimulation, with an open-population approach that would enable modelling phased implementation and gradual and incomplete uptake of the intervention. CONCLUSIONS: Economic evaluation of childhood interventions for preventing asthma will require modelling of several codependent risk factors and multiple domains that affect the diagnosis. The conceptual model can inform the development and validation of a policy model for childhood asthma prevention.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".