Assessing the quality of online information on glaucoma procedures.
Bibliographic record
Abstract
OBJECTIVE: To investigate the quality of information related to glaucoma procedures found online using 2 different assessment tools. DESIGN: Cross-sectional survey of 100 web sites found via Google search engine. METHODS: The terms "peripheral iridotomy" and "trabeculectomy" along with synonymous keywords were inputted into Google's search engine. The first 50 functional websites for each term were assessed by 2 independent raters using the DISCERN instrument as well as a quality assessment tool by the Journal of the American Medical Association (JAMA). Statistical analysis included an evaluation of intra-rater reproducibility and interclass correlation between the 2 scales. MAIN OUTCOME MEASURES: (i) Quality of web site content based on DISCERN and JAMA scores, (ii) quality of web site based on categorization of web site (iii), intra-rater reproducibility of each scale, and (iv) interclass correlation between the 2 rating scales. RESULTS: Only 22% of the web sites for peripheral iridotomy and 34% of the web sites for trabeculectomy met all the criteria for JAMA's quality assessment. The mean DISCERN scores for peripheral iridotomy and trabeculectomy were 44 and 43.7, respectively, indicating poor quality. For the DISCERN scale, level of agreement between raters for each question ranged from κ = 0.550 (95% confidence interval [CI] 0.700-1.026) to κ = 0.884 (95% CI 0.751-1.017). For the JAMA 4 scale, level of agreement for each question ranged from κ = 0.874 (95% CI 0.734-1.01) to κ = 1.00. CONCLUSION: Our study indicates that information found online for two common ophthalmic procedures is of variable and poor quality. Thus, patients may be receiving misinformation online and better measures need to be implemented to avoid the dissemination of low-quality health information.
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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 | 0.010 | 0.064 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".