Evaluating the Quality of Rosacea Studies: Implications for the Patient and Physician
Bibliographic record
Abstract
BACKGROUND: Patients suffering from rosacea may experience frequent blushing and flushing, erythema, telangiectasia, and/or rhinophyma. In an attempt to find effective treatments, many studies have been performed. OBJECTIVE: It is important to be able to evaluate the quality of clinical trials where agents have been used to treat rosacea, and to compare the effectiveness of the different therapies used for this indication. METHODS: The reports on the efficacy and safety of the different drug therapies were evaluated using predetermined criteria. We searched MEDLINE (1966-2002) for studies where rosacea was treated with the various therapies. The criteria used to assess the quality of the studies were: randomization, blinding (double, single, or open), aims clearly defined, prior sample size calculation, whether inclusion and/or exclusion criteria were outlined, baseline comparison of patient characteristics and demographics, interventions and efficacy parameters defined, compliance assessed, statistical analysis performed including intention-to-treat evaluation of efficacy of therapy. RESULTS: Using the above-mentioned criteria, each study was rated in order to determine the quality of the clinical trial. The maximum score a study could attain was 20. To determine if high-quality studies are cited more often than the lower-quality papers, the number of times a study had been cited since its publication was measured. We found 13 of the 42 studies scored greater than 14; these studies were rated as high-quality studies. There was no significant association between high-quality papers and the number of times they had been cited, suggesting that other factors are also taken into consideration when a given study is cited. CONCLUSION: It is important for investigators and clinicians to be aware of the parameters that count towards designing a high-quality protocol since such studies are more likely to reflect efficacy rates that are accurate. Furthermore, it is essential that when a study is written up, the pertinent information regarding the design of the trial and the manner in which it was conducted are conveyed to the reader.
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 | 0.572 | 0.870 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.034 | 0.029 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".