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Record W2288319791 · doi:10.22374/cjgim.v10i4.89

Unilateral Adrenal Hemorrhage and the Challenge Of Early Recognition

2015· article· en· W2288319791 on OpenAlexaffvenue
Christian Kraeker

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicAdrenal Hormones and Disorders
Canadian institutionsMcMaster UniversityMcGill University
Fundersnot available
KeywordsMedicineAdrenal HemorrhageContext (archaeology)SepsisAdrenal insufficiencyHeparinComputed tomographyInternal medicineIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

Summary Adrenal hemorrhage (AH) is a rare condition with an estimated prevalence of 1% among hospital-based autopsies. It is usually discovered incidentally on computed tomography. 1 Most cases are associated with the use of anticoagulation, especially in the setting of heparin-induced thrombocytopenia, anti-phospholipid antibody syndrome, trauma, metastatic disease, sepsis, and critical illness. 5 - 7 We report a case of acute unilateral adrenal hemorrhage associated with Escherichia coli pyelonephritis in the context of recent prolonged steroid use.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0020.001

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 head0.040
GPT teacher head0.266
Teacher spread0.226 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations1
Published2015
Admission routes2
Has abstractyes

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