Ventilatory neural drive in chronically hypercapnic patients with COPD: effects of sleep and nocturnal noninvasive ventilation
Bibliographic record
Abstract
Sleep brings major challenges for the control of ventilation in humans, particularly the regulation of arterial carbon dioxide pressure ( P aCO 2 ). In patients with COPD, chronic hypercapnia is associated with increased mortality. Therefore, nocturnal high-level noninvasive positive-pressure ventilation (NIV) is recommended with the intention to reduce P aCO 2 down to normocapnia. However, the long-term physiological consequences of P aCO 2 “correction” on the mechanics of breathing, gas exchange efficiency and resulting symptoms ( i.e. dyspnoea) remain poorly understood. Investigating the influence of sleep on the neural drive to breathe and its translation to the mechanical act of breathing is of foremost relevance to create a solid rationale for the use of nocturnal NIV. In this review, we critically discuss the mechanisms by which sleep influences ventilatory neural drive and mechanical consequences in healthy subjects and hypercapnic patients with advanced COPD. We then discuss the available literature on the effects of nocturnal NIV on ventilatory neural drive and respiratory mechanics, highlighting open avenues for further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".