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Record W2144032762 · doi:10.1159/000334639

Clinical Performance Targets and Quality of Life in Hemodialysis Patients

2011· article· en· W2144032762 on OpenAlexaff
Albert H.A. Mazairac, G. Ardine de Wit, Muriël P.C. Grooteman, E. Lars Penne, Neelke C. van der Weerd, Claire H. den Hoedt, Renée Lévesque, Marinus A. van den Dorpel, Menso J. Nubé, Piet M. ter Wee, Peter J. Blankestijn, Michiel L. Bots

Bibliographic record

VenueBlood Purification · 2011
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsHemodialysisIntensive care medicineMedicineQuality of life (healthcare)Internal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: Patients value health-related quality of life (HRQOL) over survival. It was our aim to study the relation between attainment of widely accepted performance targets and HRQOL in hemodialysis patients. METHODS: This study included baseline data from 715 hemodialysis patients from 29 dialysis centers. Six clinical performance targets, as recommended by the Kidney Disease Outcomes Quality Initiative (KDOQI), were evaluated: single-pool Kt/V (≥1.2), hemoglobin (11-13 g/dl), vascular access (fistula), phosphorus (2.3-4.5 mg/dl), parathyroid hormone (150-300 pg/ml), and blood pressure (predialysis <140/90 and postdialysis <130/ 80 mm Hg). RESULTS: After correction for case-mix and multiple comparisons, no association was found between the 6 KDOQI clinical performance targets and the 14 HRQOL domains, or between the number of performance targets reached and HRQOL. CONCLUSION: Attainment with widely accepted clinical performance targets was not related to the HRQOL of hemodialysis patients. Hence, in clinical guidelines, HRQOL should be adopted as an explicit treatment goal for these individuals.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.304
Teacher spread0.241 · 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 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

Citations29
Published2011
Admission routes1
Has abstractyes

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