Appraisal of online health information on intravenous iron: Are patients well‐informed?
Bibliographic record
Abstract
This study aimed to evaluate the quality, content, and readability of top-listed websites on intravenous (IV) iron. We conducted online searches for the term "iron infusion" using Google, Bing, and Yahoo. The search term was determined based on Google Trends data for March 2025. Five gynecologists independently reviewed the websites using the DISCERN instrument, Journal of the American Medical Association (JAMA) benchmarks, and Abbott's Scale. Credibility and readability were further assessed using the Health on the Net (HON) Foundation Code of Conduct Certification, the Simple Measure of Gobbledygook (SMOG), the Flesch-Kincaid Grade Level (FKGL), and the Flesch-Kincaid Read Ease formula (FRES). A total of 25 websites were included. The mean DISCERN score was moderate (2.9 ± 0.9 out of 5), with only three websites (12%) providing excellent quality information. Seven (28%) websites were classified as having poor to very poor quality information. The mean JAMA score was low (2.1 ± 1.3 out of 4), and only three websites (12%) met all benchmarks. The overall content score on Abbott's scale was low (45.4 ± 16.1 out of 106). The average authorship score (2.1 ± 1.2 out of 3) and esthetics rating (5.4 ± 1.2 out of 10) were moderate. Notably, 21 (84%) websites were not certified by the HONcode. Readability levels were poor, as reflected by the FRES (38.1 ± 16.9 out of 100), the FKGL (equivalent grade, 12.4 ± 2.8), and the SMOG (equivalent grade, 12.7 ± 2.4). Although information on IV iron is widely available online, its overall quality and content are generally poor and not easily accessible to the public. This underscores the opportunity for knowledge translation efforts aimed at developing online resources that are both accurate and understandable.
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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 | 0.008 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".