Quality and Readability of Google Search Information on HoLEP for Benign Prostate Hyperplasia
Bibliographic record
Abstract
Objective: To assess the quality and readability of online information on holmium laser enucleation of the prostate in managing benign prostate hyperplasia using the most-used search engine worldwide, Google. Methods: Google search terms “Holmium laser surgery” and “enlarged prostate” were used to generate 150 search results. Two independent authors (i) excluded any paywall, scientific literature, or advertisement and (ii) conducted an independent assessment on information quality, which was based on DISCERN, QUEST, and JAMA criteria, and readability, which was based on the FKG, GFI, SMOG, and FRE scores on qualified webpages. A third author was involved if there were any discrepancies between the assessments. Results: 107 qualified webpages were included in the data analysis. The median DISCERN score was 42 out of 80 (IQR 35–49). The median JAMA score was 0 out of 4 (IQR 0–1). The median QUEST score was 9 out of 28 (IQR 9–12). Using the non-parametric ANOVA and post hoc Games–Howell test, significant differences were identified between rankings of webpages. Sponsorship had no influence on the quality of webpages. The overall readability level required a minimum reading level of grade 11. Linear regression analysis showed that a higher ranked webpage is a positive predictor for all three quality assessment tools. Conclusions: The overall quality of online information on HoLEP is poor. We identify that the top-ranked google searches have a higher DISCERN score and are a positive predictor for DISCERN/QUEST/JAMA. Quality online information can benefit patients but should be used in conjunction with professional medical consultation.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".