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CASE REPORT: Intravenous fentanyl patient-controlled analgesia for perioperative treatment of neuropathic/ischaemic pain in haemodialysis patients: a case series

2010· article· en· W1900471216 on OpenAlexaboutno aff
Μενέλαος Καρανικόλας, Διαμάντω Αρέθα, Panagiotis Kiekkas, G. Monantera, Ioannis A. Tsolakis, Kriton S. Filos

Bibliographic record

VenueJournal of Clinical Pharmacy and Therapeutics · 2010
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFentanylAnesthesiaPerioperativeNeuropathic pain

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVE: Use of opioids is common in perioperative haemodialysis patients because they often suffer from intractable ischaemic or neuropathic lower extremity pain. Intravenous (IV) fentanyl, patient-controlled analgesia (PCA) does not appear to have been evaluated in this setting; hence this study. METHODS AND RESULTS: This is a prospective, single-centre study. IV fentanyl PCA was used for pain control in 16 patients with lower extremity, neuropathic/ischaemic pain, scheduled for major lower extremity amputation. IV fentanyl PCA was used before and after amputation in eight patients, before but not after amputation in seven patients, and until death in one terminal cancer patient who chose to forgo surgery. Pain intensity was assessed with the Visual Analogue Scale (VAS) and the McGill Pain Questionnaire. Depth of sedation was assessed on a 4-point scale. Ischaemic pain scores were high before fentanyl PCA started, but decreased significantly and remained low with fentanyl PCA use (P<0·001). Phantom pain scores were low (VAS≤4). Respiratory depression was not a problem in any patient. CONCLUSIONS: Concerns about accumulation of active opioid metabolites make provision of adequate analgesia problematic in haemodialysis patients scheduled for amputation, and emergency surgery. Our data on a small patient population suggest that IV fentanyl PCA is safe and effective for severe pain in haemodialysis patients.

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)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.388
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 teacher head, not a consensus.

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

Citations12
Published2010
Admission routes1
Has abstractyes

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