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Record W4200080090 · doi:10.1097/aln.0000000000004054

Postoperative Management of Lung Transplant Recipients in the Intensive Care Unit

2021· review· en· W4200080090 on OpenAlexaff
Matteo Di Nardo, Jussi Tikkanen, Shahid Husain, L.G. Singer, Marcelo Cypel, Niall D. Ferguson, Shaf Keshavjee, Lorenzo Del Sorbo

Bibliographic record

VenueAnesthesiology · 2021
Typereview
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineIntensive care medicineIntensive care unitLung transplantationMultidisciplinary approachMechanical ventilationIntensive careTransplantationMultidisciplinary teamLungSurgeryNursingInternal medicine

Abstract

fetched live from OpenAlex

The number of lung transplantations is progressively increasing worldwide, providing new challenges to interprofessional teams and the intensive care units. The outcome of lung transplantation recipients is critically affected by a complex interplay of particular pathophysiologic conditions and risk factors, knowledge of which is fundamental to appropriately manage these patients during the early postoperative course. As high-grade evidence-based guidelines are not available, the authors aimed to provide an updated review of the postoperative management of lung transplantation recipients in the intensive care unit, which addresses six main areas: (1) management of mechanical ventilation, (2) fluid and hemodynamic management, (3) immunosuppressive therapies, (4) prevention and management of neurologic complications, (5) antimicrobial therapy, and (6) management of nutritional support and abdominal complications. The integrated care provided by a dedicated multidisciplinary team is key to optimize the complex postoperative management of lung transplantation recipients in the intensive care unit.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.898
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.096
GPT teacher head0.414
Teacher spread0.318 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations48
Published2021
Admission routes1
Has abstractyes

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