Expert advice on helping people with obstructive sleep apnea and excessive daytime sleepiness: a plain language summary
Bibliographic record
Abstract
Plain Language SummaryWhat is this plain language summary about?For this study, a Delphi panel of 10 experts discussed and agreed upon recommendations for doctors and other healthcare providers about considerations to make when providing care for people who are very sleepy during the day (that is, the patients have excessive daytime sleepiness) and have sleep apnea.How to say (download PDF and double click sound icon to play sound)…Apnea: AP-nee-uhConsensus: kuhn-SEN-suhsDelphi: DEL-fieWhat are the key takeaways?The panel reached consensus on 32 statements. The Delphi panel agreed that excessive daytime sleepiness is when a patient says they have trouble staying alert while they are awake. Healthcare providers should use appropriate tests to check for this symptom when a patient is first tested for sleep apnea, from 6 weeks to 3 months after starting a new treatment for sleep apnea, and otherwise at least once per year along with regular follow-up visits after the patient has started treatment for sleep apnea. The panel also agreed that even after ensuring patients use their treatment for sleep apnea as prescribed for 3 to 6 months, some patients still feel sleepy during the day. In these cases, healthcare providers should think about other potential causes of the sleepiness and consider other diagnostic tests or using medicine to help these patients stay alert during the day. These recommendations may improve the care of patients by assisting healthcare providers in diagnosing patients, making decisions about treatments, and explaining the benefits of treatment to patients.Delphi panel: A group of experts on a certain topic with the goal of reaching a consensus on answers to questions related to the topic.Consensus: A majority agreement between the experts.This is an abstract of the Plain Language Summary of Publication article.View the full Plain Language Summary PDF of this article to read the full-textLink to original article here
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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) | 0.000 | 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 | 0.001 |
| 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 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".