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Record W2033778558 · doi:10.1111/sdi.12372

An Approach to Pain Management in End Stage Renal Disease: Considerations for General Management and Intradialytic Symptoms

2015· article· en· W2033778558 on OpenAlexaff
Holly M. Koncicki, Frank Brennan, Katie Vinen, Sara N. Davison

Bibliographic record

VenueSeminars in Dialysis · 2015
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineIntensive care medicinePopulationHeadachesEnd stage renal diseaseDiseaseKidney diseaseQuality of life (healthcare)Physical therapyInternal medicineSurgeryNursing

Abstract

fetched live from OpenAlex

The prevalence and severity of symptoms in patients with advanced chronic kidney disease is higher than those of the general population and comparable to those with other chronic and serious medical conditions. Despite the prevalence and severity in this population, symptoms continue to be under-recognized and inadequately managed. The recognition of specific intradialytic pain syndromes such as pain related to arteriovenous access, headaches, muscle cramps or generalized pain by providers may aid in improving patient compliance and quality of life. The approach to pain management in end stage renal disease patients follows that of the general population with specific considerations regarding clearance and potential side effects guiding selection of agents. Overall, evidence is limited regarding the pharmacology of many medications in this population.

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)0.005
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.004
Open science0.0020.003
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.289
Teacher spread0.262 · 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

Citations37
Published2015
Admission routes1
Has abstractyes

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