Bibliographic record
Abstract
There is really little disagreement in the two accounts (Orr et al. 2014; Younes, 2014) if one considers that I addressed the increase in loop gain (LG) when the upper airway (UA) is unstable (hereafter called Actual LG) whereas Orr et al. addressed only that component of LG that is measured when UA is stabilized, the component I called Chemical LG. To put these two loop gains in the proper perspective I refer to several investigations from the laboratory of the opposing authors. Wellman et al. (2011) and Edwards et al. (2012) measured the Actual LG in obstructive sleep apnoea (OSA) patients by relating the increase in ventilation at airway opening to the reduction in ventilation during the event. The values reported ranged from 1.4 to 14.0 (average 4.4). This is an order of magnitude greater than the Chemical LG (∼0.4; Younes et al. 2001; Wellman et al. 2004, 2008; Jordan et al. 2005). As indicated earlier (Younes, 2014), this difference results from an increased delay in getting a ventilatory response because the airway refuses to open until a threshold chemical drive, the ‘effective recruitment threshold’, is reached (Younes et al. 2007, 2012). Clearly, this increase in LG is a consequence of OSA. What is called a high LG in the absence of UA instability, and what is emphasized as a cause of OSA by Orr et al. is a Chemical LG of 0.7 with a maximum of 0.9 (Wellman et al. 2008). Even this relatively small increase in Chemical LG is the result of untreated OSA since it disappears with treatment (Loewen et al. 2009; Salloum et al. 2010). Orr et al. argue that this modest increase nonetheless contributes to the severity of OSA. I have no problem with this, and have in fact indicated so in my article (Younes, 2014). However, I believe that Orr et al. have exaggerated this clinical importance. They cite two papers in support of effectiveness of oxygen. The paper by Chowdhuri et al. (2010) simply demonstrated a reduction, with oxygen, in hypocapnic ventilatory response to CO2 in normal subjects. The study by Wellman et al. (2008) found >50% reduction in the apnoea–hypopnoea index (AHI) when breathing oxygen in only three out of six patients selected from a base of 43 patients because they had a high LG (range 0.4–0.9). Some of this AHI reduction was due to prolongation of apnoeic events and to disappearance of central/mixed events, which contributed ∼15% of baseline events. Thus the number of true obstructive events that were eliminated by oxygen must have been small. The acetazolamide evidence is also quite unconvincing. The 2013 study by Edwards et al. did not address the severity of OSA. In the 2012 study by Edwards et al. acetazolamide caused a reduction in AHI in patients already treated by continuous positive airway pressure (CPAP) and in whom Chemical LG was probably normal (Loewen et al. 2009; Salloum et al. 2010). So, it is not possible to attribute the improvement in AHI to a reduction in Chemical LG. It is important to note that both acetazolamide (Edwards et al. 2012) and oxygen breathing (Chowdhuri et al. 2010) increase baseline ventilation, and hence respiratory drive. Their helpful effects in some patients need not reflect a reduction in Chemical LG but may be because chemical drive needs to increase less to reach the effective recruitment threshold for UA opening. Readers are invited to give their views on this and the accompanying CrossTalk articles in this issue by submitting a brief comment. Comments may be posted up to 6 weeks after publication of the article, at which point the discussion will close and authors will be invited to submit a ‘final word’. To submit a comment, go to http://jp.physoc.org/letters/submit/jphysiol;592/14/2907 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. None declared.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.029 |
| Insufficient payload (model declined to judge) | 0.030 | 0.023 |
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".