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Record W3093947568 · doi:10.5812/aapm.105754

The Role of Opioids in Pain Management in Elderly Patients with Chronic Kidney Disease: A Review Article

2020· review· en· W3093947568 on OpenAlexaff
Sanam Dolati, Faezeh Tarighat, Fariba Pashazadeh, Kavous Shahsavarinia, Saina Gholipouri, Hassan Soleimanpour

Bibliographic record

VenueAnesthesiology and Pain Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineOxycodoneHydromorphoneTramadolKidney diseaseChronic painOpioidPain ladderAcetaminophenMethadoneBuprenorphineAnalgesicIntensive care medicineAnesthesiaInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) is a global public health problem. Pain is one of the most generally experienced symptoms by CKD patients. Pain management is a key clinical activity; nonetheless, insufficient pain management by health professionals keeps it up. Opioids as pain relievers are a class of naturally-derived and synthetic medications. They act through interactions with receptors in peripheral nerves. Numerous pharmacokinetic alterations happen with aging that influence drug disposition, metabolism, and quality of life. Acetaminophen alone, or combined with low-potency opioid dose is regarded as the safest pain-relieving choice for CKD. Morphine and codeine are probably eluded in renal impairment patients and used with excessive carefulness. Tramadol, oxycodone, and hydromorphone can be used by patient monitoring, while methadone, transdermal fentanyl, and buprenorphine seem to be safe to use in older non-dialysis patients with renal impairment. Consistent with the available literature, the main aim of this review was to explore the occurrence of chronic pain and its opioid treatment in CKD patients. According to this review, more and well-made randomized controlled trials are necessary to find appropriate opioid doses and explore the occurrence of side effects.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.268
Teacher spread0.257 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations44
Published2020
Admission routes1
Has abstractyes

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