Quality of Web-based Information for the 10 Most Common Fractures
Bibliographic record
Abstract
BACKGROUND: In today's technologically advanced world, 75% of patients have used Google to search for health information. As a result, health care professionals fear that patients may be misinformed. Currently, there is a paucity of data on the quality and readability of Web-based health information on fractures. OBJECTIVES: In this study, we assessed the quality and readability of Web-based health information related to the 10 most common fractures. METHODS: Using the Google search engine, we assessed websites from the first results page for the 10 most common fractures using lay search terms. Website quality was measured using the DISCERN instrument, which scores websites as very poor (15-22.5), poor (22.5-37.5), fair (37.5-52.5), good (52.5-67.5), or excellent (67.5-75). The presence of Health on the Net code (HONcode) certification was assessed for all websites. Website readability was measured using the Flesch Reading Ease Score (0-100), where 60-69 is ideal for the general public, and the Flesch-Kincaid Grade Level (FKGL; -3.4 to ∞), where the mean FKGL of the US adult population is 8. RESULTS: Overall, website quality was "fair" for all fractures, with a mean (standard deviation) DISCERN score of 50.3 (5.8). The DISCERN score correlated positively with a higher website position on the search results page (r(2)=0.1, P=.002) and with HONcode certification (P=.007). The mean (standard deviation) Flesch Reading Ease Score and FKGL for all fractures were 62.2 (9.1) and 6.7 (1.6), respectively. CONCLUSION: The quality of Web-based health information on fracture care is fair, and its readability is appropriate for the general public. To obtain higher quality information, patients should select HONcode-certified websites. Furthermore, patients should select websites that are positioned higher on the results page because the Google ranking algorithms appear to rank the websites by quality.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".