Single breath count test and its applications in clinical practice: a systematic review
Bibliographic record
Abstract
Background: Single breath count test (SBCT) may be a reproducible, rapid, easy to perform and easy to interpret substitute to spirometry especially in low resource settings for certain conditions. Its interest has been rekindled with the recent COVID-19 pandemic and it can be done as a part of tele-medicine as well. Objectives: The objective of this review was to summarize the evidence of SBCT in clinical practice. Methods: The authors searched EMBASE, PubMed and Google Scholar for all the relevant articles as per exclusion and inclusion criteria. Two authors independently screened all the studies. Newcastle Ottawa Scale was used to assess the quality of the studies. The systematic review was carried following the PRISMA guidelines. Results: After the rigorous process of screening, a total of 13 articles qualified for the systematic review. SBCT greater than 25 had sensitivity of greater than 80% in diagnosing myasthenia gravis exacerbation and SBCT less than or equal to 5 predicted the need for mechanical ventilation in Guillain-Barre syndrome (GBS) patients with 95.2% specificity. Also, Single breath count correlated significantly with forced expiratory volume in 1 sec (FEV1) and forced vital capacity (FVC) in children with pulmonary pathology and in patients with COVID-19 it was used to rule out the need for noninvasive respiratory support. Conclusion: SBCT will undoubtedly be an asset in low resource settings and in tele-medicine to assess the prognosis and guide management of different respiratory and neuromuscular diseases.
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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 | 0.017 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".