The Association of Serotonin Toxicity with Combination Linezolid–Serotonergic Agent Therapy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Linezolid (LZD) has a longstanding reported association with the onset of serotonin toxicity (ST), secondary to drug–drug interactions with serotoninergic agents. There have been no conclusive data supporting the incidence or contributing risk factors to date. The study evaluated the incidence of ST in patients treated with LZD and serotonergic agents concomitantly versus LZD alone. The secondary objectives included a comparison of ST incidence in patients treated with one serotonergic agent + LZD versus two or more serotonergic agents + LZD. The studies used for this meta-analysis were retrieved from PubMed, Scopus, and Google Scholar. All studies including a comparison between LZD alone and LZD + a serotonergic agent published between 1 January 2000 and 1 October 2023 and meeting the quality standards were considered for inclusion. Fourteen studies were identified, with five meeting all inclusion and exclusion criteria with no significant heterogeneity. For the analysis of LZD monotherapy vs. SA combination therapy, four studies with 6025 patients total were analyzed and yielded an odds ratio of 1.78 (CI [1.04, 3.02]; I2 = 49%; GRADE certainty: low). Four studies and 2501 patients were included in the analysis of one versus more than one SA with an odds ratio of 5.18 (CI [1.05, 25.49]; I2 = 44.87; GRADE certainty: moderate). The Newcastle–Ottawa score, visual inspection of the funnel plot, and Egger’s statistic were used to evaluate quality and heterogeneity. The Peto method was used to calculate the summary odds ratios. All analyses were performed using Comprehensive Meta-Analysis version 3.0 and R, while GRADE was used to evaluate the quality of the final recommendation. The number of concomitant serotonergic agents may play a role in the development of serotonin toxicity in patients prescribed linezolid. In patients requiring linezolid therapy and serotonergic agents, risk versus benefit analysis should pay attention to the number of interacting drugs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| 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 | 0.000 |
| 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".