Meta-analysis of factors influencing the progression of cough variant asthma (CVA)
Bibliographic record
Abstract
Objective: To comprehensively evaluate the influencing factors of cough variant asthma \n(CVA) disease progression by meta-analysis, and to provide a reference for clinical work. \nMethods: comprehensive retrieve relevant assessment of cough variant asthma (CVA), the \ninfluence factors of disease progression of literature researchers a number of independent \nscreens and quality evaluation, literature into literature, data extraction, into the literature of \nquality evaluation of the Newcastle - Ottawa (Newcastle - Ottawa Scale, NOS) Scale, Metaanalysis \nusing RevMan 5.3 software, effect size for odds ratio (OR). Results: a total of 664 \npatients with CVA were included in the 7 English pieces of literature and 3 Chinese pieces \nof literature, among which 195 patients developed typical asthma.Family history of asthma \n[OR=14.12,95%Cl (7.79,25.59)],allergic condition [OR=5.70,95%Cl(2.02,16.13)],low FEV1/ \nFVC[MD=-4.44,95%Cl (-5.28, -2.91)] were risk factors for the development of cough variant \nasthma (CVA).Conclusion: family history of asthma, allergic constitution, and low FEV1/FVC \nare risk factors for the progression of cough variant asthma (CVA). However, the mechanism of \nCVA progression to typical asthma is still unclear, and whether airway hyperresponsiveness is \na risk factor is still controversial and needs to be further explored by clinicians and researchers
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.047 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".