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Record W4306248158 · doi:10.1510/mmcts.2022.077

Thoracoscopic right-S10 segmentectomy: Alternative posterior approach

2022· article· en· W4306248158 on OpenAlexaff
George Rakovich

Bibliographic record

VenueMultimedia Manual of Cardio-Thoracic Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineBronchusDissection (medical)TrunkRadiologyAnatomySurgeryLungInternal medicine

Abstract

fetched live from OpenAlex

Individual basal segmentectomies can be particularly challenging. The author has previously used an anterior approach when performing S10 segmentectomies. However, he finds that a posterior approach allows direct access to the vein and bronchus, which is further aided by dividing the posterior portion of the S6-S10 intersegmental plane. The trunk of the inferior pulmonary vein now becomes a convenient landmark because it hugs and delineates the segmental bronchus while it courses posteriorly. The author currently favors this approach for all S10 segmentectomies. Cross-sectional imaging and/or 3-dimensional reconstructions are essential in delineating anatomic relationships, in particular the relationship of the vein and its branches (which may vary) and the bronchus. They are also useful for locating the segmental artery, which typically lies just deep to the bronchus. Imaging is used both as a tool for preoperative planning and as a guide during operative dissection. Regardless of the approach, the S10 remains a difficult segmentectomy. Great care is required while dissecting and dividing delicate bronchovascular structures located deep within the operative field.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.004

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 head0.027
GPT teacher head0.302
Teacher spread0.275 · 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
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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