What Is the Evidence for Co-trimoxazole, Clindamycin, Doxycycline, and Minocycline in the Treatment of Methicillin-Resistant <i>Staphylococcus aureus</i> (MRSA) Pneumonia?
Bibliographic record
Abstract
Objective: To review the evidence for trimethoprim-sulfamethoxazole (TMP-SMX), clindamycin, doxycycline, and minocycline in the treatment of methicillin-resistant Staphylococcus aureus (MRSA) pneumonia. Data Source: MEDLINE, PubMed, EMBASE, Google, Google Scholar, Cochrane Central Register of Controlled Trials from 1946 to May 20, 2019. The search was performed with the keywords methicillin-resistant Staphylococcus aureus, MRSA, Staphylococcus aureus, pneumonia, trimethoprim, sulfamethoxazole drug combination, trimethoprim, sulfamethoxazole, TMP-SMX, co-trimoxazole, clindamycin, doxycycline, and minocycline. Data Extraction: Studies reporting the use of the above antibiotics for MRSA pneumonia treatment with clinical outcomes were included. Search parameters were limited to English language and human studies only. Data Synthesis: The search yielded 16 relevant articles: 6 TMP-SMX, 8 clindamycin, zero doxycycline, and 2 minocycline. For TMP-SMX, prospective randomized trials showed variable results; however, these studies were not specifically designed to assess MRSA pneumonia treatment. Retrospective studies with clindamycin suggested that it could be used as monotherapy or in combination with other anti-MRSA antibiotics. There was no evidence for doxycycline use, but 2 small retrospective reviews appeared to support minocycline as a treatment option. Relevance to Patient Care and Clinical Practice: These antibiotics are often used in clinical practice as potential treatment options for MRSA pneumonia. This article reviews the evidence for the clinical efficacy and safety of these agents. Conclusions: There are limited data to support use of TMP-SMX, clindamycin, doxycycline, or minocycline in MRSA pneumonia treatment. Randomized controlled trials are required to determine the effectiveness of these antibiotics. Clinicians should base their decision to use these agents on a case-by-case basis depending on clinical status and susceptibility results.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".