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Record W2001911660 · doi:10.1503/cjs.010612

The role of the laparoendoscopic single site totally extraperitoneal approach to inguinal hernia repairs: a review and meta-analysis of the literature

2014· review· en· W2001911660 on OpenAlexvenueno aff
M. Siddiqui, Maksym Kovzel, S. Brennan, Oliver Priest, Shaun R. Preston, Yuen Soon

Bibliographic record

VenueCanadian Journal of Surgery · 2014
Typereview
Languageen
FieldMedicine
TopicHernia repair and management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInguinal herniaMeta-analysisGeneral surgeryHerniaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Laparoendoscopic single site (LESS) surgery may have perceived benefits of reduced visible scarring compared to conventional laparoscopic (LAP) totally extraperitoneal (TEP) hernia repairs. We reviewed the literature to compare LESS TEP inguinal hernia repairs with LAP TEP repairs. METHODS: We searched electronic databases for research published between January 2008 and January 2012. RESULTS: A total of 13 studies reported on 325 patients. The duration of surgery was 40-98 minutes for unilateral hernia and 41-121 minutes for bilateral repairs. Three studies involving 287 patients compared LESS TEP (n = 128) with LAP TEP (n = 159). There were no significant differences in operative duration for unilateral hernias (p = 0.63) or bilateral repairs (p = 0.29), and there were no significant differences in hospital stay (p > 0.99), intraoperative complications (p = 0.82) or early recurrence rates (p = 0.82). There was a trend toward earlier return to activity in the LESS TEP group (p = 0.07). CONCLUSION: Laparoendoscopic single site surgery TEP hernia repair is a relatively new technique and appears to be safe and effective. Advantages, such as less visible scarring, mean patients may opt for LESS TEP over LAP TEP. Further studies with clear definitions of outcome measures and robust follow-up to assess patient satisfaction, return to normal daily activities and recurrence are needed to strengthen the evidence.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.712
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.286
Teacher spread0.226 · 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 designNot applicable
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

Citations20
Published2014
Admission routes1
Has abstractyes

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