Different definitions in childhood asthma: how dependable is the dependent variable?
Bibliographic record
Abstract
There is abundant literature on how to select and statistically deal with predictors in prediction models. Less attention has been paid to the choice of the outcome. We assessed the impact of different asthma definitions on prevalence estimates and on the prediction model's performances. We searched PubMed and extracted data of definitions used to diagnose childhood asthma (between 6 and 18 yrs) in cohort studies. Next, using data from an ongoing cohort study (n = 186), we constructed and compared four prediction models which all predict asthma at age 6 yrs, using a fixed set of predictors and four different definitions in turn. We defined an area of clinical indecision (posterior probability between 25% and 60%) and calculated the number of children who remained inside this area. 122 papers yielded 60 different definitions. Prevalence estimates varied between 15.1% and 51.1% depending on the asthma definition used. The percentage of children whose posterior asthma probability was in the area of clinical indecision varied from 14.9% to 65.3%. Variation in definitions and its effect on the performance of prediction models may be another source of otherwise inexplicable variation in daily clinical decision making. More uniformity of operational asthma definitions seems needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".