Optimal method for assessment of respiratory muscle strength in neuromuscular disorders using sniff nasal inspiratory pressure (SNIP)
Bibliographic record
Abstract
BACKGROUND: The ability to accurately determine respiratory muscle strength is vitally important in patients with neuromuscular disorders (NMD). Sniff nasal inspiratory pressure (SNIP), a test of inspiratory muscle strength, is easier to perform for many NMD patients than the more commonly used determination of maximum inspiratory pressure measured at the mouth (MIP). However, due to an inconsistent approach in the literature, the optimal technique to perform the SNIP maneuver is unclear. Therefore, we systematically evaluated the impact of performing the maneuver with nostril contralateral to the pressure-sensing probe open (SNIPOP) versus closed (SNIPCL), on determination of inspiratory muscle strength in NMD patients as well as control subjects with normal respiratory muscle function. METHODS: NMD patients (n = 52) and control subjects without respiratory dysfunction (n = 52) were studied. SNIPOP, SNIPCL, and MIP were measured during the same session and compared using ANOVA. Agreement and bias were assessed with intraclass correlation coefficients (ICC) and Bland-Altman plots. RESULTS: Mean MIP values were 58.2 and 94.0 cmH2O in NMD and control subjects, respectively (p<0.001). SNIPCL was greater than SNIPOP in NMD (51.9 ±31.0 vs. 36.9 ±25.4 cmH2O; p<0.001) as well as in controls (89.2 ±28.1 vs. 69.2 ±29.2 cmH2O; p<0.001). In both populations, the ICC between MIP and SNIPCL (NMD = 0.78, controls = 0.35) was higher than for MIP and SNIPOP (NMD = 0.53, controls = 0.06). In addition, SNIPCL was more often able to exclude inspiratory muscle weakness than SNIPOP. CONCLUSIONS: SNIPCL values are systematically higher than SNIPOP in both normal subjects and NMD patients. Therefore, SNIPCL is a useful complementary test for ruling out inspiratory muscle weakness in individuals with low MIP values.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".