Tissue around catheters is a niche for bacteria associated with medical device infection
Bibliographic record
Abstract
OBJECTIVE: To investigate whether pericatheter tissue is an additional niche for bacteria potentially causing catheter-associated infections in humans. DESIGN: Postmortem patient study. SETTING: Intensive care unit, autopsy room, and microbiological laboratory in a university hospital. PATIENTS: Eighteen deceased patients from whom 35 catheters plus surrounding tissues were collected. INTERVENTIONS: Under axenic conditions catheters and surrounding tissue were excised from deceased intensive care unit patients. The excised parts of the catheters and samples of surrounding tissue were quantitatively cultured and bacteria identified, and tissue histology/immunohistochemistry was performed. MEASUREMENTS AND MAIN RESULTS: Nine of the 35 (26%) pericatheter tissue samples tested were highly culture positive. The corresponding catheter segments were culture negative or yielded only low numbers of bacteria. Bacteria cultured from different sites of the catheter and surrounding tissues almost all were coagulase-negative staphylococci (predominantly Staphylococcus epidermidis) and Enterococcus faecalis. In histology, bacteria were seen in tissue, intercellularly and associated with host phagocytes. CONCLUSIONS: Tissue surrounding biomedical devices forms a niche for bacteria. This is an as yet nonrecognized element in the pathogenesis of catheter-associated infections, with possible consequences for strategies of prevention and treatment of these 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".