Bibliographic record
Abstract
Stimulation of upper airway (UA) muscles during sleep by isolated transcranial magnetic stimulation (TMS) twitch can improve airflow dynamics without arousal, but the effect of repetitive TMS (rTMS) on UA dynamics is unknown. Phrenic nerve magnetic stimulation (PNMS) can be used to produce painless experimental twitch-induced flow limitation during wakefulness. The aim of this study was to quantify the effects of rTMS applied during wakefulness on UA mechanical properties using PNMS in subjects with obstructive sleep apnoea (OSA). Phrenic nerve magnetic stimulation was applied to 10 subjects, with and without simultaneous rTMS, during inspiration and expiration. Flow-limitation characteristics and UA obstruction level were determined [maximal (V̇I,max)and minimal inspiratory airflow (V̇I,min),V̇I,max-V̇I,min flow drop (ΔV̇I),oropharyngeal (POro,peak ) and velopharyngeal peak pressures, oropharyngeal k1 /k2 ratios with k1 and k2 determined by the polynomial regression model between instantaneous flow and pharyngeal pressure and UA resistance]. Both genioglossus and diaphragm root mean squares and motor-evoked potential amplitudes (geniolossus, GGAmp ) and latencies were computed. A flow-limitation pattern always occurred after PNMS. A decrease in V̇I,max and an increase in ΔV̇I occurred following rTMS applied during inspiration, and POro,peak values were more negative with both inspiratory and expiratory rTMS. The GGAmp also increased significantly from the second to the last rTMS expiratory train twitch. All other parameters remained unchanged. These results suggest the following conclusions: (i) rTMS does not improve UA mechanical properties in awake subjects with OSA; (ii) the activation pattern of UA muscles differs following isolated twitch and repetitive cortical stimulation of the genioglossus; and (iii) rTMS applied during expiration induces corticomotor facilitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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; both teacher heads agree on what is shown here.
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".