MétaCan
Menu
Back to cohort

Causes and consequences of inflammation on anemia management in hemodialysis patients

2009· review· en· W2098534434 on OpenAlexvenueno aff
Lakhmir S. Chawla, Mahesh Krishnan

Bibliographic record

VenueHemodialysis International · 2009
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersAmgen
KeywordsMedicineHemodialysisAnemiaIntensive care medicineInflammationInternal medicine

Abstract

fetched live from OpenAlex

Inflammation is common among hemodialysis patients, and evidence is accumulating to suggest that inflammation is a major contributor to morbidity and mortality. Several factors have been suggested as potential causes of inflammation, including infections and the atherosclerosis process, as well as etiologies directly related to kidney disease such as reduced renal function and dialysis. Among several inflammatory biomarkers investigated, serum C-reactive protein (CRP) is the most widely used. In hemodialysis patients, raised CRP levels have been shown to be predictive of cardiovascular events, hospitalization, and all-cause and cardiovascular mortality. Elevated CRP levels may correlate with comorbidities and intercurrent events, all of which may impact the response to erythropoiesis-stimulating agents (ESAs) and lead to higher ESA doses. Most dialysis facilities do not routinely measure CRP, despite recommendations by the National Kidney Foundation's Kidney Disease Outcomes Quality Initiative. Regular measurement of CRP levels may help providers to understand change in ESA dosing and identify patients at risk for cardiovascular events. This review explores the inter-relationships between inflammation, CRP levels, and anemia management in patients receiving hemodialysis.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.316
Teacher spread0.288 · 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 designOther design
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

Citations39
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueHemodialysis InternationalSame topicDialysis and Renal Disease ManagementFrench-language works237,207