Characterization and reliability of internet resources on pulmonary rehabilitation for individuals with chronic lung disease
Bibliographic record
Abstract
BACKGROUND: Individuals with lung disease commonly use the internet as a source of health information on pulmonary rehabilitation (PR). The objective of this study was to characterize internet resources on PR, and to assess the content, readability, and quality of patient-directed PR resources. METHODS: The first 200 websites for the search term 'pulmonary rehabilitation resources and exercise' were analyzed on Google, Yahoo, and Bing. Website content was assessed based on 30 key components of PR from the 2013 and 2021 international consensus statements. Website quality was determined using DISCERN, JAMA benchmarks, and Global Quality Scale (GQS). RESULTS: 66 unique PR websites were identified with the two most common categories being scientific resources (39%) and foundation/advocacy organizations (33%). The average reading level of websites was 11 ± 3. PR content varied significantly across websites (mean range 13.4-21.5). Median DISCERN total score and GQS score were 4 (IQR 3-4) and 3.5 (IQR 2-4), respectively, representing moderate-good quality. Foundation/advocacy websites had higher DISCERN and GQS scores compared to other websites. CONCLUSION: Foundation/advocacy websites had the highest quality and reliability metrics; however, the higher-than-recommended reading levels may compromise patient comprehension and utilization. This study provides critical insight on the current state of online PR health-related information.
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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".