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Record W2560168816 · doi:10.1213/xaa.0000000000000424

Anesthetic Considerations for Pneumonectomy With Left Atrial Resection on Cardiopulmonary Bypass in a Patient With Lung Cancer

2016· article· en· W2560168816 on OpenAlex
Katherine Marseu, Leonid Minkovich, Marijana Zubrinic, Shaf Keshavjee

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 designCase report
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".

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueA & A Case Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineCardiopulmonary bypassPneumonectomyLung cancerLeft atriumSurgeryPulmonary veinAnesthesiaLungAnestheticResectionCardiologyInternal medicineAtrial fibrillation

Abstract

fetched live from OpenAlex

Cases of pneumonectomy plus atrial resection for lung cancer have been reported in the surgical literature, but not the anesthesia literature. To achieve curative resection, cardiopulmonary bypass (CPB) may be necessary. Although CPB may complicate the management of these high-risk patients, these cases should always be undertaken in a center where it is immediately available. Here, we discuss the anesthetic management of a 70-year-old man with left lower lobe lung cancer invading the left inferior pulmonary vein and left atrium.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.309

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 head0.015
GPT teacher head0.282
Teacher spread0.267 · 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