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Record W1979861473 · doi:10.1097/aln.0b013e31819fac6a

Modified and Newly Designed Right-sided Double-lumen Endobronchial Tubes Are Complementary

2009· letter· en· W1979861473 on OpenAlexaboutno aff
Jean S. Bussières, Jacques Somma

Bibliographic record

VenueAnesthesiology · 2009
Typeletter
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLumen (anatomy)Surgery

Abstract

fetched live from OpenAlex

University Heart and Lung Institute at Laval Hospital, Quebec City, Quebec, Canada. jean.bussieres@anr.ulaval.caWe read with great interest the case report1on the application of a newly designed right-sided, double-lumen endobronchial tube (R-DLT) in patients with a very short right mainstem bronchus.However, in citing our work2on the improvement of the endobronchial positioning of the R-DLT, Hagihira et al. stated that we modified the design of the bronchial cuff and that these changes seem to offer little improvement. This statement is inconsistent with our published manuscript which demonstrates, on a randomized series of 80 patients, that the modified enlarged area of the lateral orifice (and not the bronchial cuff as stated by Hagihira et al. ) improve the success rate of final positioning from 74 to 97% with a P < 0.0109. These two new versions of the R-DLT are not intended to solve the same problem, but the final objective, improvement of the use of R-DLT, is similar.We thank Dr. Hagihira for this interesting case report. While this new R-DLT may become a useful tool for thoracic anesthesiologists, we would first encourage them to validate its use with a randomized study.University Heart and Lung Institute at Laval Hospital, Quebec City, Quebec, Canada. jean.bussieres@anr.ulaval.ca

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.033
GPT teacher head0.274
Teacher spread0.242 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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