MétaCan
Menu
Back to cohort
Record W2530899144

The Use of Energy in VATS Lobectomy.

2016· article· en· W2530899144 on OpenAlexaff
Eric Goudie, Mehdi Tahiri, Moïshe Liberman

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineVATS lobectomyThoracotomySurgeryDissection (medical)Lung cancerCardiothoracic surgeryPneumonectomyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

VATS lobectomy is a safe and effective treatment strategy for operable stage I and II lung cancer. It has a similar five-year survival compared to open lobectomy (thoracotomy). VATS lobectomy is associated with less postoperative complications and shorter hospital length of stay when compared to lobectomy by thoracotomy. VATS lobectomy has not been widely adopted by the thoracic surgical community, in part, due to technical reasons. Pulmonary artery branch manipulation in VATS lobectomy is one of the most critical parts of the procedure, especially when endostaplers are used for ligation and division of the vessel. Energy devices have improved in recent years, and their application for VATS lobectomy is gaining traction. There is more and more evidence supporting the safety of ultrasonic shears to seal and divide small pulmonary artery branches. These devices are smaller and finer than endostaplers and have the potential to reduce the risk of PA injury. These more user-friendly devices may enable thoracic surgeons who are currently performing lobectomy by thoracotomy to transition to VATS. Energy devices are also useful for hilar dissection and mediastinal lymph node dissection in VATS lobectomy.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.044
GPT teacher head0.219
Teacher spread0.176 · 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 designObservational
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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicTracheal and airway disordersFrench-language works237,207