Local Infection after Placement of Percutaneous Endoscopic Gastrostomy Tubes: A Prospective Study Evaluating Risk Factors
Bibliographic record
Abstract
BACKGROUND: Due to its high efficacy and technical simplicity, percutaneous endoscopic gastrostomy (PEG) has gained wide-spread use. Local infection, occurring in approximately 2% to 39% of procedures, is the most common complication in the short term. Risk factors for local infection are largely unknown and therefore--apart from calculated antibiotic prophylaxis--preventive strategies have yet to be determined. OBJECTIVE: To assess the potential patient- and procedure-related risk factors for peristomal infection following PEG tube placement. METHODS: Potential patient-related (eg, age, sex, diseases, body mass index, concomitant antibiotic therapy) and procedure-related (endoscopist experience, institutional factors, findings on endoscopy) risk factors and their coincidence with local infection, defined as a positive peristomal infection three days after PEG tube placement, were evaluated at two institutions. A standardized antibiotic prophylaxis was not performed. The peristomal infection score was also evaluated in 390 patients. RESULTS: Using a multivariate binary regression analysis, four risk factors were established as relevant for local infection after PEG: clinical institution (OR 6.69; P = 0.0001), size of PEG tubes (15 Fr versus 9 Fr; OR 2.12; P = 0.05), experience of the endoscopist (more than 100 investigations versus less than 100 investigations; OR 0.54; P = 0.05) and the existence of a malignant underlying disease (OR 2.28; P = 0.019). CONCLUSIONS: Similar to other endoscopic interventions, local infection as a complication of PEG tube placement depends on the experience of the endoscopist. Institutional factors also play a significant role. Additional risk factors include PEG tube size and underlying diseases. These findings indicate that the local infection after PEG tube placement may be influenced by both endoscopy-associated factors and by the underlying disease status of the patient.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".