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Record W4385406174 · doi:10.26355/eurrev_202307_33151

The role of peritoneal lavage in benign gynecologic laparoscopic surgery.

2023· article· en· W4385406174 on OpenAlexaff
Carlo De Cicco Nardone, Fernando Ficarola, Francesco Plotti, Roberto Montera, L. Feole, Stefania Rampello, Gabriele Messina, Daniela Luvero, Roberto Marci, Roberto Angioli, Claudio Terranova

Bibliographic record

VenuePubMed · 2023
Typearticle
Languageen
FieldMedicine
TopicAbdominal Surgery and Complications
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsMedicineKetorolacSalineSurgeryLaparoscopyPeritoneal cavityLaparoscopic surgeryAnesthesiaAbdominal cavityAnalgesic

Abstract

fetched live from OpenAlex

OBJECTIVE: Laparoscopic surgery offers many advantages compared to invasive surgery but one of the main problems is postoperative pain, partially resulting from the peritoneal inflammatory process mediated by inflammatory cytokines. The rationale of this study is that intraperitoneal washing could remove inflammatory mediators that are the cause of postoperative pain and could help in the removal of CO2 from the abdominal cavity. This article aims to analyze the effects of peritoneal lavage in the reduction of postoperative shoulder pain. PATIENTS AND METHODS: 277 patients enrolled to undergo laparoscopic gynecologic surgery were included in the study. Women are randomized into two groups, according to the use or non-use of peritoneal lavage with saline solution at the end of laparoscopic gynecological major procedures. RESULTS: Data show that the peritoneal lavage can significantly reduce postoperative pain in the first 36 hours after surgery, as well as patients' requests for analgesics: during the first 3 postoperative days, requests for paracetamol were lower in the YW (Yes Washing) group than the NW (No Washing) group (77 vs. 101; p<0.05); similar results are obtained considering ketorolac administration (62 vs. 71; p<0.05). CONCLUSIONS: Peritoneal lavage after gynecological laparoscopic procedures may be effective in the reduction of postoperative pain and use of analgesics.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.244
Teacher spread0.216 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2023
Admission routes1
Has abstractyes

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