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Record W2136611200 · doi:10.1164/rccm.201202-0193ed

Extracorporeal Membrane Oxygenation as “Bridge” to Lung Transplantation: What Remains in Order to Make It Standard of Care?

2012· letter· en· W2136611200 on OpenAlexaff
Lorenzo Del Sorbo, V. Marco Ranieri, Shaf Keshavjee

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2012
Typeletter
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of Toronto
FundersFondazione per la Ricerca sulla Fibrosi Cistica
KeywordsMedicineExtracorporeal membrane oxygenationLung transplantationIntensive care medicineTransplantationExtracorporealLungSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Since its introduction into clinical practice, lung transplantation\n(LTx) is gradually becoming a worldwide standard treatment for\npatients with a broad spectrum of end-stage respiratory diseases\n(1–3). From 1995 to 2010, more than 30,000 LTx have been\nperformed, and it is worth noting that in recent years the number\nof LTx has been progressively increasing to more than\n3,000/year in 2010, with a post-transplant graft half-life that\nwent from 4.7 in the 1990s to 5.9 in the new millennium (4).\nHowever, the crude mortality rate of patients awaiting LTx is\nhigher than mortality for other solid organs. Mortality rate in\n2009 for patients on the waiting list for LTx was about 14.1% in\nNorth America (www.srtr.org) and 14.7% in Italy (www.airt.it).\nWhat are the reasons for these unacceptable mortality rates?\nFirst, patients have to wait for the graft longer than patients\nwaiting for other organs because of the small number of lungs\nsuitable for transplantation (5). Second is the lack of supportive\ntherapies that are able to replace respiratory function when\nthe primary pulmonary diseases evolve from “respiratory insufficiency”\nto “respiratory failure,” characterized by refractory\nhypoxemia and hypercapnia.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.658
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.369
Teacher spread0.341 · 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.

Study designOther design
Domainnot available
GenreCommentary

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

Citations27
Published2012
Admission routes1
Has abstractyes

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