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Record W2919576556 · doi:10.2215/cjn.05180418

Clinical Pharmacology Considerations in Pain Management in Patients with Advanced Kidney Failure

2019· article· en· W2919576556 on OpenAlexaff
Sara N. Davison

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2019
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineMedical prescriptionIntensive care medicineQuality of life (healthcare)Chronic painPain managementManaged careKidney diseaseAcute painHealth carePhysical therapyAnesthesiaInternal medicinePharmacologyNursing

Abstract

fetched live from OpenAlex

Pain is common and poorly managed in patients with advanced CKD, likely due to both under and over prescription of appropriate analgesics. Poorly managed pain contributes to patients' poor quality of life and excessive health care use. There is tremendous variability within and between countries in prescribing patterns of analgesics, suggesting that factors other than patient characteristics account for these differences. This article discusses the pharmacologic management of acute and chronic pain in patients with advanced CKD, and the role analgesics, including opioids, play in the overall approach to pain management.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.349
Teacher spread0.329 · 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

Citations122
Published2019
Admission routes1
Has abstractyes

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