Con: can physiological risk factors for obstructive sleep apnea be determined by analysis of data obtained from routine polysomnography?
Bibliographic record
Abstract
The following argument was prepared in response to the question without the knowledge of the contents of the opposing argument. The answer is CLEARLY NO. The mechanisms of ventilatory instability in obstructive sleep apnea (OSA) are complex [1, 2], and defining them with confidence requires interventions that are not practical in routine polysomnography [3–5]. If it is not possible to measure instability traits from routine polysomnograms (PSGs), how is it that since 2015 papers and reviews are being published at an impressive rate in top-rated journals using an approach that claims to estimate all the stability factors from standard clinical PSGs? All these papers are based on a method proposed by Terrill et al. in 2015 to measure loop gain (LG) [6], and subsequently “improved” to provide more and more traits [7]. The basic approach is to measure ventilation in the hyperpneic phase between obstructive events (Vdrive). Without arousal, Vdrive is assumed to reflect chemical drive at the end of apnea. Arousal, if present, is assumed to contribute a fraction of Vdrive. A model with four variables (LG, time constant of chemical responses, circulatory delay, and the arousal contribution) is used to partition Vdrive into the components related to arousal (a constant predicted from the model; Varousal) and that related to the chemical drive (Vchem). The model is then used to estimate the time course of the chemical drive during the obstructed phase. From this LG, the chemical drive preceding arousal (arousal threshold), airway collapsibility, and pharyngeal muscle compensation are derived [8]. In our view, this model is based on untenable assumptions and does not identify the traits that are truly relevant to ventilatory instability in OSA:
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".