Dupuytren Disease: Is There Enough Comprehensive Patient Information on the Internet?
Bibliographic record
Abstract
BACKGROUND: Dupuytren disease is a chronic nonmalign fibroproliferative disorder that causes finger contractures via proliferation of new tissue under the glabrous skin of the hand, resulting in multiple functional limitations for the patient. As many surgical therapy options exist, patients suffering from this condition actively search for information in their environment before consulting a health professional. OBJECTIVE: As little is known about the quality of Web-based patient information, the aim of this study was to conduct its systematic evaluation using a validated tool. METHODS: A total of 118 websites were included, and qualitative and quantitative assessment was performed using the modified Ensuring Quality Information for Patients (EQIP) tool. This standardized and reproducible tool consists of 36 items to assess available information in three categories: contents, identification, and structure data. Scientific data with restricted access, duplicates, and irrelevant websites were not included. RESULTS: Only 32 websites addressed more than 19 items, and the scores did not significantly differ among the website developers. The median number of items from the EQIP tool was 16, with the top websites addressing 28 out of 36 items. The quality of the newly developed websites did not increase with passing time. CONCLUSIONS: This study revealed several shortcomings in the quality of Web-based information available for patients suffering from Dupuytren disease. In the world of continuously growing and instantly available Web-based information, it is the health providers' negligence of the last two decades that there are very few good quality, informative, and educative websites that could be recommended to patients.
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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 | 0.024 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".