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Record W2594222752 · doi:10.21037/jovs.2017.01.01

Video-assisted and minimally-invasive open chest surgery for the treatment of mediastinal tumors and masses

2017· review· en· W2594222752 on OpenAlexaff
George Rakovich, Jean Deslauriers

Bibliographic record

VenueJournal of Visualized Surgery · 2017
Typereview
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsUniversité LavalUniversité de Montréal
Fundersnot available
KeywordsMedicineMediastinumInvasive surgeryMinimally invasive proceduresOpen surgeryCardiothoracic surgeryThoracoscopyRadiologySurgeryGeneral surgery

Abstract

fetched live from OpenAlex

This article reviews the anatomy of the mediastinum as well as indications, limitations, techniques, and results of video-assisted thoracic surgery (VATS) and other minimally-invasive open-chest surgery approaches currently used for the surgical management of mediastinal tumors and masses. It is written by two surgeons with vastly different backgrounds and thoracic surgical experience. One of them is young and very familiar with VATS approaches and technologies while the other is a senior surgeon relatively unfamiliar with minimally-invasive techniques. This combination of authorship is ideal to analyze the pros and cons of the use of minimally-invasive approaches for the surgical management of mediastinal lesions such as thymic epithelial tumors (TETs) or neurogenic tumors. This is important because several thoracic surgeons have expressed concerns about the ability of thoracoscopic procedures to maintain adherence to sound oncological principles.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.250
GPT teacher head0.465
Teacher spread0.215 · 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 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

Citations11
Published2017
Admission routes1
Has abstractyes

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