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Record W2494537687 · doi:10.2215/cjn.12631115

Supportive Care: Time to Change Our Prognostic Tools and Their Use in CKD

2016· article· en· W2494537687 on OpenAlexaff
Cécile Couchoud, Brenda R. Hemmelgarn, Peter Kotanko, Michael J. Germain, Olivier Moranne, Sara N. Davison

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2016
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineIntensive care medicineQuality of life (healthcare)MEDLINEHealth careCertaintyClinical PracticeDialysisDiseaseInternal medicineFamily medicineNursing

Abstract

fetched live from OpenAlex

In using a patient-centered approach, neither a clinician nor a prognostic score can predict with absolute certainty how well a patient will do or how long he will live; however, validated prognostic scores may improve accuracy of prognostic estimates, thereby enhancing the ability of the clinicians to appreciate the individual burden of disease and the prognosis of their patients and inform them accordingly. They may also facilitate nephrologist's recommendation of dialysis services to those who may benefit and proposal of alternative care pathways that might better respect patients' values and goals to those who are unlikely to benefit. The purpose of this article is to discuss the use as well as the limits and deficiencies of currently available prognostic tools. It will describe new predictors that could be integrated in future scores and the role of patients' priorities in development of new scores. Delivering patient-centered care requires an understanding of patients' priorities that are important and relevant to them. Because of limits of available scores, the contribution of new prognostic tools with specific markers of the trajectories for patients with CKD and patients' health reports should be evaluated in relation to their transportability to different clinical and cultural contexts and their potential for integration into the decision-making processes. The benefit of their use then needs to be quantified in clinical practice by outcome studies including health-related quality of life, patient and caregiver satisfaction, or utility for improving clinical management pathways and tailoring individualized patient-centered strategies of care. Future research also needs to incorporate qualitative methods involving patients and their caregivers to better understand the barriers and facilitators to use of these tools in the clinical setting. Information given to patients should be supported by a more realistic approach to what dialysis is likely to entail for the individual patient in terms of likely quality and quantity of life according to the patient's values and goals and not just the possibility of life prolongation.

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)0.049
metaresearch head score (Gemma)0.103
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.049
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.103
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0080.013
Open science0.0020.006
Research integrity0.0030.008
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 head0.227
GPT teacher head0.453
Teacher spread0.226 · 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
GenreCommentary

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

Citations47
Published2016
Admission routes1
Has abstractyes

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