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Record W2937472915 · doi:10.1152/physiol.00052.2018

How Do Kidneys Adapt to a Deficit or Loss in Nephron Number?

2019· review· en· W2937472915 on OpenAlexaff
Hadi Fattah, Anita T. Layton, Volker Vallon

Bibliographic record

VenuePhysiology · 2019
Typereview
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversity of Waterloo
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesU.S. Department of Veterans Affairs
KeywordsNephronBiologyKidneyCell biologyEndocrinologyInternal medicinePhysiologyMedicine

Abstract

fetched live from OpenAlex

A deficit or loss in the number of nephrons, the functional unit of the kidney, can induce compensatory growth and hyperfunction of remaining nephrons. An increase in single nephron glomerular filtration rate (SNGFR) aims to compensate but may be deleterious in the long term. The increase in SNGFR is determined by the dynamics of nephron loss, total remaining GFR, the body's excretory demand, and the functional capacity to sustain single nephron hyperfunction.

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)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.094
GPT teacher head0.383
Teacher spread0.290 · 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

Citations71
Published2019
Admission routes1
Has abstractyes

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