Continuous Positive Airway Pressure in Severe Obstructive Sleep Apnea Reduces Pain Sensitivity
Bibliographic record
Abstract
STUDY OBJECTIVE: To evaluate effects of CPAP on pain sensitivity in severe OSA patients. DESIGN: Within-subject treatment study. SETTING: Hospital-based sleep disorders center. PATIENTS: Twelve severe OSA patients (7 men, 5 women), 50.2 ± 12.5 years, with no pain. INTERVENTIONS: The morning after a diagnostic nocturnal polysomnogram (NPSG), patients underwent a training session of finger withdrawal latency (FWL) testing to a radiant heat stimulus, a validated human behavioral model of thermal nociception. Baseline FWL in seconds was obtained after the training session. CPAP pressure was titrated on a second night in the laboratory. Two nights after titration, patients returned to sleep in the laboratory on CPAP. FWL was tested in the morning after awakening, after 6-8 wks of CPAP use, and finally (within 6-8 weeks) after 2 nights of discontinuation of CPAP. Mean FWL in seconds (sec) was compared using MANOVAs with nights as the within subject variable. RESULTS: Apnea-hypopnea index (AHI) decreased from 50.9 ± 14.5 to 1.4 ± 1.0 with CPAP, and sleep continuity improved. In parallel, FWL increased significantly from a mean baseline of 9.8 ± 1.3 sec to 13.7 ± 5.1 sec (P = 0.01) and with continued CPAP use (5.1 ± 2.3 h nightly) for 6-8 weeks FWL remained elevated (21.1 ± 16.2 sec). After the 2-night CPAP discontinuation, apnea/hypopneas returned and sleep was fragmented (AHI = 32.6 ± 19.8). FWL decreased to 11.6 ± 5.9 sec relative to intermediate-term CPAP use (P = 0.03). CONCLUSION: CPAP treatment reduces pain sensitivity in OSA patients. Future studies will focus on patients with OSA and chronic pain and identify mediating mechanisms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".