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Record W4386032875 · doi:10.1510/mmcts.2023.061

Thoracoscopic left upper division (S1/2/3) resection: alternative posterior approach

2023· article· en· W4386032875 on OpenAlexaff
George Rakovich

Bibliographic record

VenueMultimedia Manual of Cardio-Thoracic Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineBronchusDissection (medical)PerioperativeParenchymaRight Main BronchusSurgeryPneumonectomyLungRadiologyResectionInternal medicineRespiratory diseasePathology

Abstract

fetched live from OpenAlex

A minimally invasive pulmonary segmentectomy allows adequate oncological treatment in selected cases while preserving lung parenchyma and minimizing perioperative morbidity and length of hospital stay. Most lung segments may be resected as segmentectomies or as part of bisegmentectomies (as is the case for the lingula). In the author's experience, left upper division resection (S1, S2, S3 trisegmentectomy) may be challenging. Because the lingula and lingular structures need to be preserved, they may obstruct visualization and hamper the movement of the dissecting instruments. This has been the author's experience using an anterior approach. In contradistinction, a posterior approach allows direct access to the artery and arterial branches and greatly facilitates access to the segmental bronchus. Dissection of the bronchus proceeds from back to front, away from the artery. In addition, when we are isolating and encircling the bronchus, we have already freed the artery from the bronchus and it is safely out of the way. The advantages of a posterior approach are particularly apparent when pathological nodes between the bronchus and artery make the dissection tedious, as in the case presented. Regardless of the surgical approach, S1/S2/S3 trisegmentectomy remains a challenging procedure that requires great care in its execution.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.353
Teacher spread0.312 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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