When Patients Fall Asleep in the Dental Chair — A Wake-up Call for Dentists
Bibliographic record
Abstract
Excessive daytime sleepiness (EDS) can be a symptom of several underlying disorders. Dental professionals are in a unique position to observe and recognize EDS in their patients. Recognizing EDS and referring patients for diagnosis can be life-saving. Dentists can also be involved in the treatment of some disorders associated with EDS. What should we think of a patient who dozes off in the waiting room or a treatment chair? As health providers, should we be concerned? Possible scenarios for patients who fall asleep at the dental office range from situations where patients may be acutely fatigued and simply “catching up” in a comfortable environment to situations where patients may be chronically sleepy and suffering from a pathologic and treatable condition. Sleepiness occurs in 5% to 13% of the general population. 1 Falling asleep in the dental environment may be a sign of EDS. Common causes of EDS are listed in Table 1. 2 Why should we be concerned about sleepy patients? Patients who are chronically sleepy are a danger to themselves and others. A few moments of careful consultation could enable dentists to direct patients with EDS toward treatment that might improve quality of life, decrease cardiovascular morbidity and, ultimately, save lives. These are high expectations for a brief consultation. Sleep deprivation has become common in society. Many people are chronically fatigued. Patients who are sleepy put their own lives and the lives of others at risk. When patients with EDS drive or operate machinery, chances of accidents increase. A person who stays awake for 24 hours has the performance equivalence of a person with a blood alcohol level of 0.10%.3
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".