Single count breath test for the evaluation of respiratory function in Myasthenia Gravis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Myasthenia gravis (MG) can have a variety of respiratory presentations, ranging from mild symptoms through to respiratory failure. The evaluation of respiratory function in MG can be limited by accessibility to testing facilities, availability of medical equipment, and facial weakness. The single count breath test (SCBT) may be a useful adjunct in the evaluation of respiratory function in MG. METHOD: A systematic review of the databases PubMed, EMBASE, and the Cochrane Library was conducted from inception to October 2022 in accordance with PRISMA guidelines and was registered on PROSPERO. RESULTS: There were 6 studies that fulfilled the inclusion criteria. The described method of evaluating SCBT involves inhaling deeply, then counting at two counts per second, in English or Spanish, sitting upright, with normal vocal register, until another breath needs to be taken. The identified studies support that the SCBT has a moderate correlation with forced vital capacity. These results also support that SCBT can assist the identification of MG exacerbation, including via assessment over the telephone. The included studies support a threshold count of ≥ 25 as consistent with normal respiratory muscle function. Although further analysis is needed, the included studies describe the SCBT as a quick bedside tool that is inexpensive and well tolerated. CONCLUSIONS: The results of this review support the clinical utility of the SCBT in assessing respiratory function in MG and describe the most current and effective methods of administration.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| 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".