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Record W2914650226 · doi:10.1111/hdi.12736

Chronic pain is underestimated and undertreated in dialysis patients: A retrospective case study

2019· article· en· W2914650226 on OpenAlexaffvenue
Orit Kliuk‐Ben Bassat, Silviu Brill, Haggai Sharon

Bibliographic record

VenueHemodialysis International · 2019
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineChronic painItchingDialysisKidney diseaseRetrospective cohort studyHemodialysisIncidence (geometry)Adverse effectCohortInternal medicineSurgeryPhysical therapy

Abstract

fetched live from OpenAlex

Significant chronic pain is highly prevalent in chronic kidney disease patients and is associated with morbidity and mortality. In this study, we retrospectively evaluated the incidence and treatment of pain in the dialysis unit of our tertiary referral center. The cohort included 147 patients. Over 66% reported significant (VAS >40) chronic pain during the preceding 3 months, most often characterized as stabbing (38%) and with concurrent itching (44%). Only 33% of patients received chronic pain medications, while 55.6% of patients with severe pain and 45.9% with pain characterized as the worst imaginable did not receive any analgesics. Pregabalin or weak opioids were the most frequently used. In conclusion, chronic pain is highly prevalent and markedly undertreated in dialysis patients, despite its significant adverse impact.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.276
Teacher spread0.264 · 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 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

Citations16
Published2019
Admission routes2
Has abstractyes

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