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Record W2606237119 · doi:10.23907/2016.028

Liver Pathology in First Presentation Diabetic Ketoacidosis at Autopsy

2016· article· en· W2606237119 on OpenAlexaff
Anita Lal, Jacqueline L. Parai, Christopher M. Milroy

Bibliographic record

VenueAcademic Forensic Pathology · 2016
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineDiabetic ketoacidosisDiabetes mellitusAutopsyKetoacidosisForensic pathologyFatty liverSteatosisCause of deathDiseaseInternal medicinePediatricsGastroenterologyPathologyType 1 diabetesEndocrinology

Abstract

fetched live from OpenAlex

Diabetes mellitus is an enormous health burden on developed and developing nations. Eight percent of people in the United States are stated to have diabetes mellitus and 79 million people have impaired glucose tolerance. Sudden death from diabetic ketoacidosis (DKA) is common and nonalcoholic fatty liver disease (NAFLD) is a frequent finding in patients with diabetes mellitus and impaired glucose tolerance. Diabetic ketoacidosis accounts for around 1% of autopsy cases in our units and 25% of these cases did not have a previous diagnosis of diabetes mellitus. We have analyzed for the presence of NAFLD in 16 patients dying on first presentation of DKA. Some degree of NAFLD was present in all cases, with all but one case having some degree of steatosis and some degree of fibrosis was present in 14 out of 16 cases, though none where cirrhotic. Inflammation was present in nine of 13 cases and glyogenated nuclei in five of 13 cases. NAFLD can be well established in patients dying of DKA who were not known to be diabetic before death. The pathology shares features with alcoholic liver disease. They should not be mistakenly diagnosed as dying of other causes of ketoacidosis based upon the liver pathology present.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.275
Teacher spread0.254 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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