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Record W4417050808 · doi:10.3390/curroncol32120670

The Evolving Role of Medical Thoracoscopy for the Management of Malignant Pleural Effusion

2025· article· en· W4417050808 on OpenAlexvenueno aff
Jean‐Baptiste Lovato, Avinash Aujayeb, Bernard Duysinx, Philippe Astoul

Bibliographic record

VenueCurrent Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicPleural and Pulmonary Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsPulmonologistsThoracoscopyMalignant pleural effusionNarrative reviewPleurodesisEndoscopyPleural effusionPleural disease

Abstract

fetched live from OpenAlex

MT is a minimally invasive endoscopic procedure which is a well-established tool for the management of pleural malignancies, which commonly cause pleural effusions. MT allows for pulmonologists to perform diagnostic and therapeutic maneuvers at the same time with high diagnostic sensitivity and can also shorten the hospitalization duration. MT, which is video-assisted, is performed by pulmonologists, and is not the same procedure as surgical thoracoscopy or video-assisted thoracoscopy surgery (VATS). To perform MT, pulmonologists use non-disposable rigid or semi-rigid telescopes in the endoscopy or theater suites under local anesthesia with intravenous conscious sedation/analgesia or mild anesthesia on a spontaneously breathing patient. MT is mainly indicated for diagnostic purposes in cases of unexplained exudative pleural effusions and/or talc pleurodesis ('poudrage') to prevent the recurrence of a persistent pleural effusion. This narrative review describes the role of this procedure in assessing potential malignant pleural disease whilst providing insights into procedural details, diagnostic performance, safety considerations, and clinical applications. In weighing the advantages and disadvantages of this procedure in comparison to alternative diagnostic and therapeutic modalities, this review aims to show the benefits of MT for this scenario. Finally, a few thoughts about future directions of this endoscopic procedure are proposed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.149

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.053
GPT teacher head0.422
Teacher spread0.368 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2025
Admission routes1
Has abstractyes

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