Sleep disorder symptoms are common and unspoken in Canadian general practice
Bibliographic record
Abstract
OBJECTIVE: Primary care patients were surveyed for what sleep disorder symptoms they discussed with their physicians. Their responses were compared with those of new Sleep clinic patients. The goal was to discover what symptom presentation leads to a successful referral to a sleep clinic. METHODS: We recruited two samples: 191 older Primary care patients and 138 Sleep clinic patients. Participants completed the Sleep Symptom Checklist (SSC). This consists of 21 symptoms in four domains: insomnia, sleep disorder, daytime symptoms and psychological distress. All respondents indicated which symptoms had been discussed with their physician in the past year. Primary care subjects were designated as Decliners (completed SSC, refused further evaluation), Dropouts [completed some evaluation steps, but not polysomnography (PSG)] and Completers (completed PSG). RESULTS: Primary care participants frequently had symptoms but relatively few had discussed them with their doctor. Sleep clinic participants discussed significantly more symptoms with their referring physician than did Primary care Dropouts or Decliners in all categories except psychological distress. Primary care Completers, 88.5% of whom were ultimately diagnosed with sleep apnoea/hypopnoea syndrome and/or periodic limb movement disorder, also discussed their sleep disorder symptoms less frequently than did Sleep clinic patients but tended to give more prominence to symptoms of insomnia and impaired daytime function. CONCLUSIONS: The findings suggest that Primary care patients often have symptoms they do not discuss, even when a primary sleep disorder exists. The brief SSC checklist, developed in our laboratory, has potential to improve the referral rates of older primary care patients who have sleep disorder.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".