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Record W2739040375 · doi:10.21037/jtd.2017.06.17

Extracorporeal support in airway surgery

2017· review· en· W2739040375 on OpenAlexaff
Konrad Höetzenecker, Walter Klepetko, Shaf Keshavjee, Marcelo Cypel

Bibliographic record

VenueJournal of Thoracic Disease · 2017
Typereview
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineExtracorporealAirwayLife supportLung transplantationSurgeryAirway obstructionIntensive care medicineDissection (medical)IntubationExtracorporeal membrane oxygenationTransplantation

Abstract

fetched live from OpenAlex

Extracorporeal life support (ECLS) is increasingly used for major airway surgery. It facilitates complex reconstructions and maintains gas exchange during endoscopic procedures in patients with critical airway obstruction. ECLS offers the advantage of an uncluttered surgical field and eliminates the need for crossing ventilation tubes, thus, making precise surgical dissection easier. ECLS is currently used for hemodynamic and respiratory support in lung transplantation as well as extended tumor resections with an acceptable risk profile. This work reviews the published experience of ECLS in airway surgery both in adults and in pediatric patients. It highlights currently available devices and their indications.

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)0.001
metaresearch head score (Gemma)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.169
GPT teacher head0.461
Teacher spread0.292 · 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
GenreReview

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

Citations97
Published2017
Admission routes1
Has abstractyes

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