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Record W1602395233 · doi:10.1159/000337836

Factors Affecting Loss of Residual Renal Function(s) in Dialysis

2012· review· en· W1602395233 on OpenAlexaff
Jochen G. Raimann, Thomas M. Kitzler, Nathan W. Levin

Bibliographic record

VenueContributions to nephrology · 2012
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineExcretory systemDialysisRenal functionEndocrine systemKidneyEndocrinologyInternal medicinePhysiologyUrologyIntensive care medicineHormone

Abstract

fetched live from OpenAlex

Many physiological processes relate to two aspects of kidney function: (1) excretory and secretory (excretion of electrolytes and water, elimination of metabolic end products and uremic toxins, regulation of the acid-base status), and (2) endocrine functions (regulating bone and mineral metabolism and red blood cell production). Decreases in renal functions are known to be associated with shortened survival. The exact mechanisms for this are still to be elucidated but evidence in the literature suggests mechanisms such as adverse effects of accumulation of uremic toxins, fluid overload, inflammation and possibly loss of antioxidant functions. Knowledge of factors affecting decrease of residual renal function is currently based on observational data only. Possible strategies to preserve residual renal function have been suggested but require confirmation in adequately powered prospective trials to test their effectiveness.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.043
GPT teacher head0.342
Teacher spread0.300 · 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 designSystematic review
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

Citations14
Published2012
Admission routes1
Has abstractyes

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