Examining the Association between Immunosuppressants and Wound Healing: A Narrative Review
Bibliographic record
Abstract
OBJECTIVE: To review how different classes of immunosuppressants affect wound healing. DATA SOURCES: A literature search was conducted in PubMed, Google Scholar, and the University of Calgary Health Sciences Library. STUDY SELECTION: The researchers initially screened article titles using key words such as "immunosuppressive medication," "wound healing," and "immunosuppression." Articles in which the title and/or abstract contained these key words, that addressed wound healing related to immunosuppressant medications, and were published after 2000 were included in the review. When human data were not available for an immunosuppressant (class), animal studies were included. DATA EXTRACTION: The 61 included articles underwent full text review and summarization. DATA SYNTHESIS: All included studies were summarized descriptively including immunosuppressive mechanism of action, study participants or subjects, and evidence of effects on wound healing. CONCLUSIONS: Corticosteroids and mechanistic target of rapamycin inhibitors most consistently demonstrate detrimental effects on wound healing. For other classes of immunosuppressants, evidence is limited with varying effects on wound healing described. Larger, high-quality studies are required to better understand the effects of immunosuppressants, including those with new mechanisms of action, to identify those with the most impact on wound healing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".