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Record W4403360871 · doi:10.7759/cureus.71280

Long-Term Efficacy and Safety of Adhesion Prevention Agents in Abdominal and Pelvic Surgeries: A Systematic Review

2024· review· en· W4403360871 on OpenAlexaff
Sergio Rodrigo Oliveira Souza Lima, Kimberly Kanemitsu, Muhammad Rashid, Vaishvik K Patel, Muhammad Ali

Bibliographic record

VenueCureus · 2024
Typereview
Languageen
FieldMedicine
TopicIntestinal and Peritoneal Adhesions
Canadian institutionsWindsor Clinical Research
Fundersnot available
KeywordsMedicineTerm (time)SurgeryGeneral surgeryIntensive care medicine

Abstract

fetched live from OpenAlex

This systematic review evaluates the long-term efficacy and safety of adhesion prevention agents in abdominal and pelvic surgeries, synthesizing data from randomized controlled trials and meta-analyses. Adhesions, common postoperative complications, can lead to significant morbidity, including chronic pain, infertility, and bowel obstruction. Various agents, including hyaluronic acid-carboxymethylcellulose films and icodextrin solutions, have been developed to mitigate these risks. Our review highlights that agents like bioresorbable membranes (Seprafilm) and icodextrin significantly reduce the incidence and severity of adhesions, particularly in high-risk surgeries. However, certain complications such as anastomotic leaks and infections are associated with some agents, emphasizing the need for careful consideration in clinical decision-making. Additionally, while these agents reduce postoperative morbidity and enhance recovery, further research is needed to assess their long-term impact, particularly regarding fertility outcomes and chronic pain. This review underscores the importance of integrating adhesion prevention agents into surgical protocols, which has the potential to reduce healthcare costs, improve patient outcomes, and optimize postoperative care pathways. Standardization of adhesion prevention practices could further enhance surgical efficiency and patient recovery, particularly in high-risk patient populations and complex surgeries.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.033
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.088
GPT teacher head0.405
Teacher spread0.316 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations9
Published2024
Admission routes1
Has abstractyes

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