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Record W2935613079 · doi:10.1097/aln.0000000000002687

Transfusion-related Acute Lung Injury in the Perioperative Patient

2019· review· en· W2935613079 on OpenAlexaff
Mark J. McVey, Rick Kapur, Christine Cserti‐Gazdewich, John W. Semple, Keyvan Karkouti, Wolfgang M. Kuebler

Bibliographic record

VenueAnesthesiology · 2019
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsUniversity Health NetworkSt. Michael's Hospital
Fundersnot available
KeywordsMedicineTransfusion-related acute lung injuryPerioperativeDiffuse alveolar damageIntensive care medicineAnesthesiaLungInternal medicinePulmonary edemaAcute respiratory distress

Abstract

fetched live from OpenAlex

Transfusion-related acute lung injury is a leading cause of death associated with the use of blood products. Transfusion-related acute lung injury is a diagnosis of exclusion which can be difficult to identify during surgery amid the various physiologic and pathophysiologic changes associated with the perioperative period. As anesthesiologists supervise delivery of a large portion of inpatient prescribed blood products, and since the incidence of transfusion-related acute lung injury in the perioperative patient is higher than in nonsurgical patients, anesthesiologists need to consider transfusion-related acute lung injury in the perioperative setting, identify at-risk patients, recognize early signs of transfusion-related acute lung injury, and have established strategies for its prevention and treatment.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.896

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 integrity0.0010.001
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 head0.026
GPT teacher head0.323
Teacher spread0.297 · 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 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

Citations47
Published2019
Admission routes1
Has abstractyes

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