A resource of potential drug targets and strategic decision‐making for obstructive sleep apnoea pharmacotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is currently no pharmacotherapy for obstructive sleep apnoea (OSA) but there is no principled a priori reason why there should not be one. This review identifies a rational decision-making strategy with the necessary logical underpinnings that any reasonable approach would be expected to navigate to develop a viable pharmacotherapy for OSA. The process first involves phenotyping an individual to quantify and characterize the critical predisposing factor(s) to their OSA pathogenesis and identify, a priori, if the patient is likely to benefit from a pharmacotherapy that targets those factors. We then identify rational strategies to manipulate those critical predisposing factor(s), and the barriers that have to be overcome for success of any OSA pharmacotherapy. A new analysis then identifies candidate drug targets to manipulate the upper airway motor circuitry for OSA pharmacotherapy. The first conclusion is that there are two general pharmacological approaches for OSA treatment that are of the most potential benefit and are practically realistic, one being fairly intuitive but the second perhaps less so. The second conclusion is that after identifying the critical physiological obstacles to OSA pharmacotherapy, there are current therapeutic targets of high interest for future development. The final analysis provides a tabulated resource of 'druggable' targets that are relatively restricted to the circuitry controlling the upper airway musculature, with these candidate targets being of high priority for screening and further study. We also emphasize that a pharmacotherapy may not cure OSA per se, but may still be a useful adjunct to improve the effectiveness of, and adherence to, other treatment mainstays.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it