A Retrospective Analysis of the Effect of Tigecycline on Coagulation Function
Bibliographic record
Abstract
A number of clinical trials demonstrated that tigecycline was effective and well tolerated in the treatment of patients with various bacterial infections, but few literatures had shown the coagulopathy induced by tigecycline. To address this concern, we performed a retrospective analysis to assess the impact of tigecycline treatment on coagulation parameters in 50 patients with bacterial infections in our hospital (Shandong Provincial Hospital, China). These patients were treated with tigecycline at Shandong Provincial Hospital in 2015-2016 at either a recommended (50 mg q12h) or a higher dose (100 mg q12h). Coagulation parameters, including Fibrinogen (FIB) levels, prothrombin time (PT), activated partial thromboplastin time (aPTT), platelet count (PLT) and D-dimer, were evaluated in order to assess the impact of tigecycline treatment in these severely infected patients. What we found was that the plasma fibrinogen (FIB) level was 4.63 ± 1.56 g/L before tigecycline treatment, and decreased to 2.92 ± 1.23 g/L during treatment, which was statistically significant (p < 0.001). The mean values of aPTT and PT were significantly increased from 39.58 ± 8.72 to 44.05 ± 10.45 s (p = 0.002), and from 15.37 ± 1.53 to 16.37 ± 2.64 s (p = 0.004), respectively. This study demonstrates that treatment of tigecycline could reduce FIB, prolong aPTT and PT. In conclusion, we advise that it is necessary for practitioners routinely monitor coagulation level in at-rick patient populations treated with tigecycline.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".