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Record W2075773623 · doi:10.1097/gox.0000000000000097

Advances in Tracheal Reconstruction

2014· review· en· W2075773623 on OpenAlexaff
Siba Haykal, Michael Salna, Thomas K. Waddell, Stefan O.P. Hofer

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2014
Typereview
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineComputer science

Abstract

fetched live from OpenAlex

SUMMARY: A recent revival of global interest for reconstruction of long-segment tracheal defects, which represents one of the most interesting and complex problems in head and neck and thoracic reconstructive surgery, has been witnessed. The trachea functions as a conduit for air, and its subunits including the epithelial layer, hyaline cartilage, and segmental blood supply make it particularly challenging to reconstruct. A myriad of attempts at replacing the trachea have been described. These along with the anatomy, indications, and approaches including microsurgical tracheal reconstruction will be reviewed. Novel techniques such as tissue-engineering approaches will also be discussed. Multiple attempts at replacing the trachea with synthetic scaffolds have been met with failure. The main lesson learned from such failures is that the trachea must not be treated as a "simple tube." Understanding the anatomy, developmental biology, physiology, and diseases affecting the trachea are required for solving this problem.

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)0.001
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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.004

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 head0.041
GPT teacher head0.346
Teacher spread0.305 · 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

Citations70
Published2014
Admission routes1
Has abstractyes

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