Revision total hip arthroplasty: An analysis of the quality and readability of information on the internet
Bibliographic record
Abstract
The demand for revision total hip arthroplasty (THA) is increasing. Information quality on the internet has been extensively analysed in relation to primary THA but no such analysis has ever been performed for revision THA. Our aim was to assess the quality and readability of this information. Three major internet search engines were searched for information on revision THA. All websites were assessed for quality of information using the DISCERN score, the Journal of the American Medical Association benchmark criteria and a novel scoring system specific to revision THA [Vancouver Revision Arthroplasty Information (VRAI) score]. Website readability was assessed, as was presence of the Health On the Net Foundation (HON) seal. The majority of websites (52%) were academic with a post-graduate reading level. Only 6.5% of websites had the HON seal. Twentyeight percent of websites had a 'good' DISCERN score and only 28% had a 'good' score with the novel VRAI scoring system. Health information websites had significantly higher rates of 'good' VRAI scores (P = 0.008). Websites with the HON seal had significantly higher DISCERN scores (P = 0.01). All governmental websites were at a reading level suitable for patient review. Information on the internet relating to revision THA is of low quality, much lower than the quality of information on primary THA. We recommend governmental websites for their readability and health information websites for their quality of information specific to revision THA. Websites with the HON seal provide higher quality information and should be recommended to patients as reading material regarding revision THA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 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) | 0.001 | 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".