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.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".