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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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.047
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 teacher head, not a consensus.

Study designObservational
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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