Clinical phenotyping in untreated paediatric and adult asthma: a systematic review
Bibliographic record
Abstract
Asthma affects around 8% of the global population. Identifying groups based on clinical characteristics (phenotypes) may improve diagnostic accuracy and drive targeted management to alter the disease trajectory. To date, most phenotyping has focused on severe asthma. Aim: Systematic review of the literature on phenotyping in untreated paediatric and adult asthma. Methods: Registered with PROSPERO (CRD42023483258). Embase, Medline, CINHAL and Cohrane databases searched on 29/12/2023 with terms: asthma* ADJ2 (naïve, untreated, “new diagnos*”, not treat*” OR) AND (endotype*, phenotyp*, “treatable trait*” OR). No limits on language, age or study type. Inclusion criteria: comparing untreated asthma clinical phenotypes. Exclusion criteria: Use of regular asthma therapy. Reported according to PRISMA using GRADE and ROBINS-I tools. Results: 467 records were identified through database searches; 167 duplicates were removed and 350 abstracts independently reviewed; 205 for full text review. 195 did not report data on or compare untreated asthma phenotypes; 10 were identified for analysis. Data on 1683 participants; minimum age 6, majority adults. Four studies used data-driven methods to identify phenotypes and six used non data-driven methods. Phenotypes were driven largely by baseline characteristic selection which varied widely across studies. Lung physiology differentiated data-driven phenotypes while atopy, fractional exhaled nitric oxide (FeNO) and blood eosinophilia differentiated non data-driven phenotypes. Conclusion: Future work looking at untreated asthma phenotyping should justify initial characteristic selection and as a minimum include lung physiology, atopy, blood eosinophilia and FeNO.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".