Systematic evaluation of obstructive sleep apnea websites on the internet.
Bibliographic record
Abstract
OBJECTIVE: To determine the quality and comprehensiveness of the websites on the Internet with information directed toward patients with obstructive sleep apnea (OSA). DESIGN: Systematic evaluation of websites on the Internet with information on OSA. SETTING: World Wide Web. METHODS: The search engine Google was queried with the term "obstructive sleep apnea." The first 50 results were evaluated. Only websites written in English and containing original information were reviewed. MAIN OUTCOME MEASURES: A data quality score (DQS) was generated by evaluating each website for the presence of 67 points of information relating to OSA. Each website was also evaluated using a Global Quality Score (GQS), the DISCERN instrument, the Flesch-Kincaid reading grade level, date of the last update, and sources of funding. RESULTS: Thirty-four websites met the inclusion criteria. The average DQS was 36 points (range 17-57). The average GQS was 2.9 (range 1-5). The average DISCERN score was 3.3 (range 1.9-4.7). The average reading grade level was 11.0 (range 5.0-15.8). Twenty-six websites (76%) provided authorship information. Twenty-five websites (74%) displayed a date of the last update. Of these, 12 websites were not updated within the past 2 years. Twenty websites (59%) provided a source of funding. CONCLUSIONS: There is a heterogeneous mixture of websites with varying quality on the Internet with information regarding OSA. Health care providers should be cognizant of the variety of information available for patients and be prepared to direct patients toward the high-quality information sources.
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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 | 0.052 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.037 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".