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Record W2418556254

[Indication and recipient selection for lung transplantation].

2003· article· en· W2418556254 on OpenAlexaff
Yoshinori Okada, Takashi Kondo

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsMedicineLung transplantationTransplantationCOPDEconomic shortageLungCadaveric spasmObstructive lung diseaseLung diseaseSurgeryDiseaseIntensive care medicineInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Lung Transplantation has become an established therapeutic modality for selected patients with end-stage lung diseases including chronic obstructive lung disease(COPD). Candidates for lung transplantation should have chronic disease for which no further medical or surgical therapy is available and survival is limited. Disease specific guidelines of lung transplant candidates for COPD patients include FEV1.0 < 25% of predicted, PaCO2 > 55 Torr or cor pulmonale. As of August 2003, 13 cadaveric lung transplantation and 31 living-donor lobar transplantation has been performed in Japan, and 38 of 44 recipients are alive. The shortage of cadaveric donors, infection and chronic rejection are the major problems in lung transplantation and every endeavor must be made to solve them.

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: none
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.008

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 head0.029
GPT teacher head0.293
Teacher spread0.263 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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