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Record W1976062571 · doi:10.4021//jmc.v3i3.577

Acute Renal Infarction: An Underdiagnosed Disorder

2012· article· en· W1976062571 on OpenAlexvenueno aff
Abdur Baig, Elie Ciril, Gabriel Contreras, Oliver Lenz, Sonia Borra

Bibliographic record

VenueJournal of Medical Cases · 2012
Typearticle
Languageen
FieldMedicine
TopicRenal and Vascular Pathologies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRenal colicFlank painEmergency departmentRadiologyIncidence (geometry)AngiographyMagnetic resonance imagingAutopsyInfarctionMagnetic resonance angiographyGross hematuriaMyocardial infarctionInternal medicinePathology

Abstract

fetched live from OpenAlex

Acute renal infarction is usually diagnosed when the triad of flank pain, hematuria and elevated lactate dehydrogenase is observed. Since the symptoms are non specific, diagnosis requires high degree of suspicion or may be missed or confused with renal colic. The incidence of ARI in emergency department visits is 0.007 % whereas in autopsy series is reported 1.4 % indicating the condition goes undiagnosed frequently. With the increase use of contrast enhanced computed tomography and magnetic resonance angiography the accuracy might be improving at the present time. doi:10.4021/jmc577w

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.047
GPT teacher head0.360
Teacher spread0.313 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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