Probiotics in surgical wound infections: current status.
Bibliographic record
Abstract
BACKGROUND: Probiotics--live microorganisms that confer a health benefit when taken in adequate amounts, usually as food supplements--are receiving renewed attention in the medical community. Some have been found to play a role in disease remediation. However, mainstream medicine and science remain divided about the validity of health claims made about them. METHODS: To clarify the potential value of probiotics, we reviewed the scientific data on their role in preventing and treating surgical infections as well as some of our own studies of the effects of certain strains of lactobacilli on surgical implant infections. PRINCIPAL FINDINGS: There is little rigorous evidence that probiotics may be beneficial in the prevention and treatment of wound infections. However, data from 3 clinical trials and from our laboratory indicate that certain strains of probiotic lactobacilli and their byproducts may help reduce infection rates in surgical patients and may ameliorate staphylococcus-related infections of surgical implants. CONCLUSION: Although there is good clinical evidence that certain probiotics may be beneficial in conditions such as diarrheal and inflammatory bowel diseases, more studies are required to apply these concepts to the prevention and treatment of wound and other surgical infections.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".