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Record W4412697147 · doi:10.1097/ipc.0000000000001481

Antibiotic Prophylaxis and Intraoperative Gram Stain Analysis for Postoperative Antibiotic Treatment in Periampullary Pathology Surgery

2025· article· en· W4412697147 on OpenAlexaff
Laura Granel Villach, A Gil Catalán, J. Sastre, Ángel Moya Herráiz

Bibliographic record

VenueInfectious Diseases in Clinical Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsMedicineAntibioticsAntibiotic prophylaxisGram stainingProphylactic antibioticSurgeryMicrobiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.414
Teacher spread0.384 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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