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Record W2990159689 · doi:10.1503/cjs.001416

A new fixation-free 3D multilamellar preperitoneal implant for open inguinal hernia repair

2017· article· en· W2990159689 on OpenAlexvenueno aff
Antonio Brescia, Federico Tomassini, Giammauro Berardi, Massimo Pezzatini, Umile Michele Cosenza, D Castiglia, Anna Dall’Oglio, Adelona Salaj, Marcello Gasparrini

Bibliographic record

VenueCanadian Journal of Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicHernia repair and management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVisual analogue scaleInguinal herniaSurgeryFixation (population genetics)ImplantHerniaPostoperative painSurgical mesh

Abstract

fetched live from OpenAlex

SUMMARY: Between September 2014 and December 2015, 32 patients with inguinal hernia were treated using a new 3D mesh in our department. This mesh is characterized by a multilamellar flower-shaped central core with a flat, large-pore polypropylene ovoid disk that has to be implanted preperitoneally. Compared with the traditional Lichtenstein procedure, we observed a shorter mean duration of surgery and a significantly lower mean visual analogue scale (VAS) postoperative pain score recorded immediately after the procedure in the 3D mesh group. The mean VAS score recoded after 4 and 8 postoperative days showed better results in the 3D mesh group than the control group. Moreover, there was reduced postoperative morbidity in the 3D mesh group than the control group, even if no patients experienced severe complications.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.314
Teacher spread0.248 · 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 designBench or experimental
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

Citations7
Published2017
Admission routes1
Has abstractyes

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