Antibiotic Prophylaxis and Intraoperative Gram Stain Analysis for Postoperative Antibiotic Treatment in Periampullary Pathology Surgery
Bibliographic record
Abstract
Introduction Infectious complications after pancreatic surgery, especially in periampullary pathology, are very frequent and involve high morbidity. It is therefore essential to administer adequate prophylaxis and to establish objective criteria for postoperative antibiotic treatment. Materials and Methods This is a prospective study that included a total of 63 patients who underwent pancreatic surgery in our center. The antibiotic prophylaxis defined after the study of bacteriobilia and resistance was amoxicillin 1000 mg/clavulanic acid 200 mg and gentamicin 240 mg. In order to establish the need for antibiotic treatment in the postoperative period, it was decided to carry out intraoperative Gram stain. Results The sensitivity of the intraoperative Gram stain to determine the presence of bacteriobilia was 100%, and the specificity was 69%. The most common infectious complications described were intra-abdominal collection in 19.7% of cases and wound infection in 10.6%. Wound isolates by frequency were as follows: Enterococcus species (20.6%) and Klebsiella species (14.7%). Conclusions The changes made in antibiotic prophylaxis in relation to isolated bacteriobilia demonstrate a decrease in the number of infectious complications. Gram stain has proven to be a valid test to determine the need for postoperative treatment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".