Oscillometry to support clinical assessment in asthmatic preschoolers: Real-life impact
Bibliographic record
Abstract
In preschoolers, asthma control is assessed clinically using history and physical examination. In certain centres, oscillometry is used to support clinical assessment; yet its clinical utility for asthma management remains to be quantified. The objectives were to determine if oscillometry, as adjunct to clinical assessment, influences asthma assessment, management and control, compared to clinical assessment alone in preschoolers. We conducted a cross-sectional study in children aged 3-5 years with a confirmed asthma diagnosis. Oscillometry-tested preschoolers were matched by propensity score to untested children. The co-primary outcomes, the likelihood of a persistent asthma phenotype and a maintenance therapy prescription at the index visit, were examined by multivariable logistic regression. Asthma control over the next year was examined by cumulative logistic regression in the nested retrospective cohort with available drug claim data. The cohort comprised 726 (249 oscillometry-tested; 477 untested) children with 57.4% male (median age: 4.6 years). Propensity score matching resulted in comparable groups. Compared to controls, oscillometry-tested children were more frequently labelled with a persistent phenotype (67% vs. 50%; adjusted OR [95% CI]: 2.34 [1.66-3.34]) with no significant difference in maintenance therapy prescription (65% vs. 58%; 1.37 [0.98-1.92]); but experienced a lower likelihood of poor control over the next year (adjusted OR [95% CI]: 0.24 [0.08-0.74]). The association between the addition of oscillometry to clinical assessment with more persistent phenotype labelling and better asthma control supports its clinical utility; no significant impact on maintenance therapy prescription was observed at the index visit.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".