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Record W2745118123 · doi:10.21037/jtd.2017.08.34

Robotic-assisted left upper lobectomy: facing the challenge head-on

2017· editorial· en· W2745118123 on OpenAlexaff
Simon R. Turner, Daniela Molena

Bibliographic record

VenueJournal of Thoracic Disease · 2017
Typeeditorial
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineResectionPneumonectomyLobeSurgeryCardiothoracic surgeryRadiologyAnatomyLungInternal medicine

Abstract

fetched live from OpenAlex

Left upper lobectomy is the most challenging pulmonary lobar resection for thoracic surgeons. The anatomy of the pulmonary artery and its relation to other hilar structures put it at risk of injury, more so than during any other lobectomy. It is, therefore, not surprising that multiple techniques for resection of the left upper lobe have been developed, via both open and minimally invasive approaches. Different techniques call for a specific order in which the hilar structures should be divided, all aiming to accomplish the same thing: a safe, efficient and oncologically optimal removal of the left upper lobe and its associated lymph nodes.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.165
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.388
Teacher spread0.357 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations3
Published2017
Admission routes1
Has abstractyes

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