Should Testing for Obstructive Sleep Apnea be Offered Routinely to Older Family Medicine Patients? A Prospective Cohort Study
Bibliographic record
Abstract
176 consecutive family medicine patients, aged 45 years and older, were tested for obstructive sleep apnea using overnight polysomnography. Participants found to have OSA were offered treatment and followed for 3 years according to usual practice in the Canadian/Province of Quebec health care system. At time of recruitment Time 1, and after 3 years, Time 2, participants completed questionnaires about sleep, health related quality of life, psychological adjustment. Additionally, health data were collected by chart review.The data set consists of an Excel file, with data for 176 participants in rows. Time 1 and Time 2 data are presented in the same row. Variables are respectively labeled with the prefixes T1 and T2. The first three rows describe the measures, variables, and the units of measure for each variable. Each subject row contains 128 variables, including for T1 and T2. The final columns contain coding to identify subgroups, treatment adherence, change in health status employed in previous analyses.Cells with missing data are left blank. Data loss from Time 1 to Time 2 was expected, as we were following a clinical sample who entered the study without a specific complaint and for whom OSA treatment acceptance and adherence are typically low. Describing the trajectory of these patients, including dropout rates, was one aim of the study.The dataset can be copied and pasted into an SPSS data file.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".