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Record W2891650217 · doi:10.1177/2054358118799690

Trends in Biopsy-Based Diagnosis of Kidney Disease: A Population Study

2018· article· en· W2891650217 on OpenAlexaffabout
Amanda Cunningham, Hallgrímur Benediktsson, Daniel A. Muruve, Ainslie M. Hildebrand, Pietro Ravani

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineKidney diseasePopulationBiopsyIncidence (geometry)Retrospective cohort studyRenal biopsyCohortDiabetes mellitusNephrologyInternal medicineEndocrinology

Abstract

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BACKGROUND: Kidney biopsy is considered the gold standard for diagnosis of renal disease. It is increasingly performed in cases of diagnostic uncertainty, including in patients with coexistent diabetes and hypertension, for which a presumptive clinical diagnosis can be made. Little is known about the incidence and distribution of biopsy-proven kidney diseases. Changes in the distribution of biopsy diagnoses over time may have significant implications for resource allocation and future research. OBJECTIVE: We studied the relative frequency of kidney diseases in Southern Alberta over the past 30 years, to determine whether the population-standardized annual biopsy rate and incidence of selected diagnostic categories have changed. We hypothesized an increasing incidence of renal biopsies and a growing proportion of nonglomerular diseases (eg, tubulointerstitial disorders) likely due to evolving indications for biopsy. Given the rise in obesity, diabetes, and aging population with chronic kidney disease (CKD), we anticipated a rise in nephroangiosclerosis and diabetic nephropathy over time. DESIGN: Retrospective population-based cohort study using the Biobank for the Molecular Classification of Kidney Disease (BMCKD). SETTING: Southern Alberta, Canada. PATIENTS: All patients who underwent renal biopsy between 1985 and 2015 in our database. MEASUREMENTS: We used descriptive and quantitative analysis to characterize demographics and biopsy-based diagnoses. METHODS: We conducted a retrospective population-based cohort study to analyze all consecutive patients who underwent at least one kidney biopsy over a 30-year period in Southern Alberta (1985-2015). We considered the first adequate biopsy. We described the annual standardized incidence of biopsy-proven kidney diseases over time and summarized associated patient characteristics. We assumed a Poisson distribution for biopsy counts and used provincial demographic information to standardize rates. RESULTS: During the study period, 6434 people (58% male; mean age: 47.9 years) underwent a kidney biopsy. The population-standardized annual biopsy rate increased from 10.8 biopsies per 100 000 person-years in the first 5 years of the study (1985-1989) to 18.2 biopsies per 100 000 person-years in the last 5 years (2010-2014). The mean age at the time of biopsy increased from 42.5 years (1985-1989) to 51.4 years (2010-2014). Glomerular diseases remained the most prevalent histopathological group, with a growing representation of diabetic kidney disease from 3.69% to 16.18%, and a relative decrease in the proportion of other glomerular diseases from 72.32% to 62.92% of glomerular diagnoses. Tubulointerstitial diseases increased from 5.87% to 7.36% of total diagnoses. LIMITATIONS: Classification schemes have changed over time, so recently recognized conditions may have been misclassified in earlier data. There was a changing group of pathologists and nephrologists over this period. Variations in interpretation and application of biopsy indications by physician may influence recorded prevalence of certain diagnoses. We do not yet have complete information on indications or patient outcomes linked to the database. CONCLUSIONS: In Southern Alberta, kidney biopsy is being utilized more frequently and in older people. Diabetic nephropathy is increasingly diagnosed, which may reflect either or both changes in the prevalence of causative factors and local biopsy practices.

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)0.000
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.020
GPT teacher head0.312
Teacher spread0.292 · 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

Citations46
Published2018
Admission routes2
Has abstractyes

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