Bibliographic record
Abstract
BACKGROUND: Rosacea is a chronic inflammatory disorder that affects 10% of the population. The prevalence of rosacea is highest among fair-skinned individuals, particularly those of Celtic and northern European descent. Since a cure for rosacea does not yet exist, management and treatment regimens are designed to suppress the inflammatory lesions, erythema, and to a lesser extent, the telangiectasia involved with rosacea. OBJECTIVES: This review outlines the treatment options that are available to patients with rosacea. METHODS: Published literature involving the treatment or management of rosacea was examined and summarized. RESULTS: Patients who find that they blush and flush frequently, or have a family history of rosacea are advised to avoid the physiological and environmental stimuli that can cause increased facial redness. Topical agents such as metronidazole, azelaic acid cream or sulfur preparations are effective in managing rosacea. Patients who have progressed to erythematotelangiectatic and papulopustular rosacea may benefit from the use of an oral antibiotic, such as tetracycline, and in severe or recalcitrant cases, isotretinoin to bring the rosacea flare-up under control. Treatment with a topical agent, such as metronidazole, may help maintain remission. Patients with ocular involvement may benefit from a long-term course of an antibiotic and the use of metronidazole gel. A surgical alternative, laser therapy, is recommended for the treatment of telangiectasias and rhinophyma. Patients with distraught feelings due to their rosacea may consider cosmetic camouflage to cover the signs of rosacea. CONCLUSIONS: With the wide variety of oral and topical agents available for the effective management of rosacea, patients no longer need to feel self-conscious because of their disorder.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".