Unsupervised home spirometry<i>versus</i>supervised clinic spirometry for respiratory disease: a systematic methodology review and meta-analysis
Bibliographic record
Abstract
Background: The number of patients completing unsupervised home spirometry has recently increased due to more widely available portable technology and the COVID-19 pandemic, despite a lack of solid evidence to support it. This systematic methodology review and meta-analysis explores quantitative differences in unsupervised spirometry compared with spirometry completed under professional supervision. Methods: We searched four databases to find studies that directly compared unsupervised home spirometry with supervised clinic spirometry using a quantitative comparison ( e.g. Bland–Altman). There were no restrictions on clinical condition. The primary outcome was measurement differences in common lung function parameters (forced expiratory volume in 1 s (FEV 1 ), forced vital capacity (FVC)), which were pooled to calculate overall mean differences with associated limits of agreement (LoA) and confidence intervals (CI). We used the I 2 statistic to assess heterogeneity, the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess risk of bias and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to assess evidence certainty for the meta-analyses. The review has been registered with PROSPERO (CRD42021272816). Results: 3607 records were identified and screened, with 155 full texts assessed for eligibility. We included 28 studies that quantitatively compared spirometry measurements, 17 of which reported a Bland–Altman analysis for FEV 1 and FVC. Overall, unsupervised spirometry produced lower values than supervised spirometry for both FEV 1 with wide variability (mean difference −107 mL; LoA= −509, 296; I 2 =95.8%; p<0.001; very low certainty) and FVC (mean difference −184 mL, LoA= −1028, 660; I 2 =96%; p<0.001; very low certainty). Conclusions: Analysis under the conditions of the included studies indicated that unsupervised spirometry is not interchangeable with supervised spirometry for individual patients owing to variability and underestimation.
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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 | 0.038 | 0.082 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".