A Refresher on Herpes Zoster, Current Status on Vaccination, and the Role of the Dermatologist
Bibliographic record
Abstract
BACKGROUND: Herpes zoster (HZ) and postherpetic neuralgia (PHN) have a significant impact on quality of life. PHN is often chronic and difficult to treat. Dermatologists have always been involved in making the diagnosis of these conditions and, most recently, teaching the need for early antiviral therapy. OBJECTIVE: With the introduction of a new vaccine, HZ and its difficult-to-treat complication PHN can be prevented or minimized. Preventive medicine is important and has been supported by dermatologists with sun safety programs. Patients receiving biologics are at increased risk of developing zoster. CONCLUSION: Dermatologists should embrace zoster vaccination and recommend routine vaccination of immunocompetent individuals > age 60 years, as well as patients of any age who are starting immunosuppressants, including biologics. Given that individuals over age 50 years are at risk for PHN and studies have shown that the vaccine's immunogenicity and safety are maintained in individuals age 50 to 59 years, vaccination in this age group may be considered. Some dermatologists may consider vaccinating their own patients, but most will likely recommend that vaccination be performed by their patients' primary care physicians.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".