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Record W4406687106 · doi:10.1097/ta.0000000000004509

Kidney organ injury scaling: 2025 update

2025· review· en· W4406687106 on OpenAlexaff
Sorena Keihani, Gail T. Tominaga, Rano Matta, Joel A. Gross, Chris Cribari, Krista L. Kaups, Marie Crandall, Rosemary A. Kozar, Nicole L. Werner, Ben L. Zarzaur, Michael Coburn, Jeremy B. Myers

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2025
Typereview
Languageen
FieldMedicine
TopicAbdominal Trauma and Injuries
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsKidneyMedicineInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT: The American Association for the Surgery of Trauma initially published the organ injury scaling for the kidney in 1989, which was subsequently updated in 2018. This current American Association for the Surgery of Trauma kidney organ injury scaling update incorporates the latest evidence in diagnosis and management of renal trauma and is based upon a multidisciplinary consensus. These changes reflect the near universal use of computed tomography for renal trauma evaluation and the widespread adoption of conservative management across all grades of renal trauma.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0080.006
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.004

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 head0.030
GPT teacher head0.392
Teacher spread0.362 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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