Quality of Chronic Obstructive Pulmonary Disease Information on the Chinese Internet: Website Evaluation Study
Bibliographic record
Abstract
BACKGROUND: The development of internet technology has greatly increased the ability of patients with chronic obstructive pulmonary disease (COPD) to obtain health information, giving patients more initiative in the patient-physician decision-making process. However, concerns about the quality of website health information will affect the enthusiasm of patients' website search behavior. Therefore, it is necessary to evaluate the current situation of Chinese internet information on COPD. OBJECTIVE: This study aims to evaluate the quality of COPD treatment information on the Chinese internet. METHODS: Using the standard disease name "" ("chronic obstructive pulmonary disease" in Chinese) and the commonly used public search terms "" ("COPD") and "" ("emphysema") combined with the keyword "" ("treatment"), we searched the PC client web page of Baidu, Sogou, and 360 search engines and screened the first 50 links of the website from July to August 2021. The language was restricted to Chinese for all the websites. The DISCERN tool was used to evaluate the websites. RESULTS: A total of 96 websites were included and analyzed. The mean overall DISCERN score for all websites was 30.4 (SD 10.3; range 17.3-58.7; low quality), no website reached the maximum DISCERN score of 75, and the mean score for each item was 2.0 (SD 0.7; range 1.2-3.9). There were significant differences in mean DISCERN scores between terms, with "chronic obstructive pulmonary disease" having the highest mean score. CONCLUSIONS: The quality of COPD information on the Chinese internet is poor, which is mainly reflected in the low reliability and relevance of COPD treatment information, which can easily lead consumers to make inappropriate treatment choices. The term "chronic obstructive pulmonary disease" has the highest DISCERN score among commonly used disease search terms. It is recommended that consumers use standard disease names when searching for website information, as the information obtained is relatively reliable.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".