MétaCan
Menu
Back to cohort
Record W1966525272 · doi:10.1136/jcp.2008.058271

Renal toxicity of therapeutic drugs

2009· review· en· W1966525272 on OpenAlexaff
Rohan John, A.M. Herzenberg

Bibliographic record

VenueJournal of Clinical Pathology · 2009
Typereview
Languageen
FieldMedicine
TopicNephrotoxicity and Medicinal Plants
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineDrugDiseaseKidney diseaseKidneyNephritisAcute tubular necrosisToxicityPathologyNephrotoxicityPharmacologyBioinformaticsInternal medicineBiology

Abstract

fetched live from OpenAlex

A number of therapeutic agents can adversely affect the kidney, resulting in tubulointerstitial, glomerular or vascular disease. Drug-induced tubulointerstitial nephritis and acute tubular necrosis are common, and are often cause by antibiotics or non-steroidal anti-inflammatory drugs. Drug-induced glomerular and vascular disease is relatively rare. This review describes the morphological patterns of drug-induced disease in the kidney. The histopathological changes are often similar to disease that is idiopathic or due to other causes, so that awareness and clinical correlation are most helpful to arrive at the aetiology.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.188
GPT teacher head0.497
Teacher spread0.310 · 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

Citations93
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical PathologySame topicNephrotoxicity and Medicinal PlantsFrench-language works237,207