Systemic glucocorticoids: important issues and practical guidelines for the dermatologist
Bibliographic record
Abstract
BACKGROUND: The potent anti-inflammatory and immunosuppressive effects of systemic glucocorticoids have led to their wide use in the treatment of dermatologic diseases. However, glucocorticoids have been designated the "archetypal double-edged sword of medicine" as a result of their various potential adverse side effects. Dermatologists face major challenges in their usage and require knowledge of both the risks related to their usage and strategies with which to manage them. OBJECTIVES: This brief review includes an evidence-based, strategic approach to the general risk management of systemic glucocorticoids with a focus on preventing glucocorticoid-induced osteoporosis (GIOP). METHODS: We assess which classes of corticosteroid are most likely to provoke allergic cross-reactions and outline the mechanism for glucocorticoid resistance. We examine how glucocorticoids both help and impair normal physiology. RESULTS: Five reactivity groups are defined, based on the structural and clinical characteristics of glucocorticoids. Tests for allergy reactions and mechanisms for glucocorticoid resistance are described. Guidelines for the prevention and treatment of GIOP are introduced. CONCLUSION: Glucocorticoids play an important teleologic role in maintaining blood glucose levels adequate for brain function by inducing a catabolic state through the production of carbohydrates at the expense of proteins and fat stores. It is hoped that the various recommendations for the protection of patients treated with systemic glucocorticoids will provide physicians with practical guidelines for prescribing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".