Chest wall kinematics measured during inspiratory threshold loading, deep breathing maneuvers and CO2 rebreathing in individuals post-stroke.
Bibliographic record
Abstract
Background: After a hemispheric stroke, a person may be unable to recruit the paretic inspiratory muscles to volitionally increase tidal volume. In contrast, such muscles can be reflexly recruited with hypercapnic stimulation. Inspiratory muscle training may be beneficial post-stroke. How inspiratory muscle loading affects the paretic and healthy chest wall musculature remains unknown. Methods: We compared the chest wall volume (Vcw) between the paretic and healthy sides in individuals with a unilateral cortical stroke using an Optoelectronic Plethysmograph during: 1) quiet breathing (QB), 2) slow deep breathing (DB), 3) maximum mandatory ventilation (MVV), 4) CO2 rebreathing (7%CO2, balance O2), and 5) inspiratory threshold loading (ITL) with load increased by 5 cm H2O every 3 breaths. Results: Six individuals (age: 58±7 years; 3:3/M:F) post-stroke participated in the study. Participants showed no Vcw difference between paretic and healthy sides during QB (VT: 692±252 ml). The VT increased with DB (VT: 2906±859 ml) and the Vcw on the healthy side was 12% larger than on the paretic side (P=0.025); the same tendency was observed with MVV (P=0.057; VT: 1700±527 ml). At the highest level of CO2 rebreathing the VT was 2413±685 ml; in contast, the Vcw of the paretic side was 11% larger than the healthy side (P=0.049). ITL resulted in a 20% greater Vcw of the healthy side (VT: 1279±554) and a tendency for more expansion of the upper chest compared to the abdomen. Conclusion: Different from CO2 rebreathing, but similar to DB, inspiratory muscle loading/training may not promote recruitment of the paretic inspiratory muscles in individuals post-stroke.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".