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Record W4406235144 · doi:10.1510/mmcts.2024.108

Pulmonary sequestration 2: left lower lobe

2025· article· en· W4406235144 on OpenAlexaff
Marie-Eve Tuchon, Denise Ouellette, Alicia N. Truchon, Véronique Caty, George Rakovich

Bibliographic record

VenueMultimedia Manual of Cardio-Thoracic Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicCongenital Diaphragmatic Hernia Studies
Canadian institutionsHôpital Maisonneuve-RosemontUniversité de Montréal
Fundersnot available
KeywordsPulmonary sequestrationMedicineEmbolizationLungRadiologySurgeryPulmonary arteryInternal medicine

Abstract

fetched live from OpenAlex

Pulmonary sequestration is a rare congenital anomaly, characterized by aberrant lung tissue supplied by an aberrant systemic artery or arteries coursing within the inferior pulmonary ligament. The intralobar variety is the most frequent form. Clinical presentation may include recurrent haemoptysis and infection. Standard treatment involves surgical resection of the affected tissue with maximal preservation of healthy lung. This video tutorial presents the surgical technique for resecting intralobar pulmonary sequestration involving the left lower lobe. Rather than attempting to skeletonize the frequently friable aberrant artery(ies), the lung is mobilized and the inferior pulmonary ligament containing the aberrant artery is isolated and "thinned out" before being divided en masse; in this way, one may decrease the risk of intraoperative arterial injury and haemorrhage. Subsequent lung resection is performed in standard fashion, in this case a left lower lobectomy. Transarterial embolization has been described both as definitive treatment and as a preoperative intervention to decrease the risk of intraoperative bleeding. In the case of preoperative embolization, one must be wary of the possibility of embolic material interfering with the application of the vascular stapler when dividing the aberrant artery, and fine embolic material should be chosen.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
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 science0.0000.000
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 head0.025
GPT teacher head0.323
Teacher spread0.299 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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