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Record W3087645985 · doi:10.7759/cureus.10491

Prophylactic Mesh Placement for the Prevention of Incisional Hernia in High-Risk Patients After Abdominal Surgery: A Systematic Review and Meta-Analysis

2020· review· en· W3087645985 on OpenAlexaff
Jawad Ahmed, Nimra Hasnain, Iayla Fatima, Farheen Malik, Muhammad Ali Chaudhary, Junaid Aḥmad, Mehreen Malik, Laraib Malik, Muhammad Osama, Mirza Zain Baig, Faisal Khosa, Faiz Y. Bhora

Bibliographic record

VenueCureus · 2020
Typereview
Languageen
FieldMedicine
TopicHernia repair and management
Canadian institutionsVancouver General Hospital
Fundersnot available
KeywordsMedicineIncisional herniaSurgeryMeta-analysisSurgical meshGeneral surgeryHerniaInternal medicine

Abstract

fetched live from OpenAlex

Background and objectivesIn high-risk populations, the efficacy of mesh placement in incisional hernia (IH) prevention after elective abdominal surgeries has been supported by many published studies.This meta-analysis aimed at providing comprehensive and updated clinical implications of prophylactic mesh placement (PMP) for the prevention of IH as compared to primary suture closure (PSC). Materials and methodsPubMed, Science Direct, Cochrane, and Google Scholar were systematically searched until March 3, 2020, for studies comparing the efficacy of PMP to PSC in abdominal surgeries.The main outcome of interest was the incidence of IH at different follow-up durations.All statistical analyses were carried out using Review Manager version 5.3 (The Nordic Cochrane Centre, The Cochrane Collaboration, 2014) and Stata 11.0 (Stata Corporation LP, College Station, TX).The data were pooled using the random-effects model, and odds ratio (OR) and weighted mean differences (WMD) were calculated with the corresponding 95% confidence interval (CI). ResultsA total of 3,330 were identified initially and after duplicate removal and exclusion based on title and abstract, 26 studies comprising 3,000 patients, were included.The incidence of IH was significantly reduced for PMP at follow-up periods of one year (OR= 0.16 [0.05, 0.51]; p=0.002;I 2 =77%), two years (OR= 0.23 [0.12, 0.45]; p<0.0001;I 2 =68%), three years (OR= 0.30 [0.16, 0.59]; p=0.0004;I 2 = 52%), and five years (OR=0.15[0.03, 0.85]; p=0.03;I 2 =87%).However, PMP was associated with an increased risk of seroma (OR=1.67 [1.10, 2.55]; p= 0.02; I 2 =19%) and chronic wound pain (OR=1.71[1.03, 2.83]; p= 0.04; I 2 = 0%).No significant difference between the PMP and PSC groups was noted for postoperative hematoma (OR= 1.04 [0.43, 2.50]; p=0.92;I 2 =0%), surgical site infection (OR=1.09[0.78, 1.52]; p= 0.62; I 2 =12%), wound dehiscence (OR=0.69[0.30, 1.62]; p=0.40;I 2 = 0%), gastrointestinal complications (OR= 1.40 [0.76, 2.58]; p=0.28;I 2 = 0%), length of hospital stay (WMD= -0.49[-1.45, 0.48]; p=0.32;I 2 =0%), and operating time (WMD=9.18[-7.17, 25.54]; p= 0.27; I 2 =80%). ConclusionsPMP has been effective in reducing the rate of IH in the high-risk population at all time intervals, but it is associated with an increased risk of seroma and chronic wound pain.The benefits of mesh largely outweigh the risk, and it is linked with positive outcomes in high-risk patients.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.020
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.062
GPT teacher head0.341
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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".

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Citations16
Published2020
Admission routes1
Has abstractyes

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