A case report of a deep surgical site infection with Terrisporobacter glycolicus/T. Mayombei and review of the literature
Bibliographic record
Abstract
BACKGROUND: There are increasing data regarding Terrisporobacter glycolicus as an emerging anaerobic pathogen. However, the few published cases to date usually report it as part of a polymicrobial infection. Here, we describe the first reported monomicrobial surgical site infection with this bacterium. Identification methods, taxonomy, and clinical management of this rarely identified pathogen are also discussed. CASE PRESENTATION: A previously healthy 66-year-old sustained an open olecranon fracture of his left arm after trauma. He subsequently underwent open reduction and internal fixation (ORIF), with insertion of an olecranon locking plate and two locking screws. Ten days after surgery, the patient developed increasing pain at the surgical site and noted green discharge from the wound. Culture of the wound discharge yielded grew a pure Gram-positive anaerobe identified by the RapidANA® microbial identification system as C. difficile (profile 000010, 99.1 % probability). Reference laboratory testing identified the isolate as T. glycolicus/mayombei (previously designated as Clostridium glycolicum/mayombei) by 16S rRNA gene sequencing and as Clostridium glycolicum by MALDI-TOF mass spectrometry. The patient received an 8-week course of moxifloxacin and metronidazole with an excellent clinical response at 12 months' follow-up. CONCLUSIONS: We describe the case of a deep surgical site infection with T. glycolicus/mayombei (formerly known as Clostridium glycolicum and Clostridium mayombei, respectively), which extends our knowledge of the clinical spectrum of this pathogen. The isolate was misidentified by phenotypic identification methods.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".