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Record W2416547215 · doi:10.1177/1715163515600674

Individualizing drug therapy in patients with diabetes and chronic kidney disease

2015· article· en· W2416547215 on OpenAlexaffvenue
Lori MacCallum

Bibliographic record

VenueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2015
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsDiabetes CanadaUniversity of Toronto
Fundersnot available
KeywordsMedicineLinagliptinAlogliptinSaxagliptinSitagliptinRenal functionKidney diseaseDialysisDiabetes mellitusRamiprilUrologyDosingPharmacologyMetforminInternal medicineType 2 diabetesEndocrinology

Abstract

fetched live from OpenAlex

CKD 1 and 2 eGFR ≥60 mL/min CKD 3 eGFR 30-59 mL/min CKD 4 eGFR 15-29 mL/min CKD 5 eGFR <15 mL/min or dialysis Comments Metformin No dose adjustment Reduce dose Use alternative agent Risk of drug accumulation with declining renal function, especially if acute.Alpha-glucosidase inhibitor Acarbose No dose adjustment No dose adjustment Use alternative agent DPP4-inhibitors Alogliptin No dose adjustment Lower dose 12.5 mg daily (30-49 mL/min) Lower dose 6.25 mg daily Can be given without regard to timing of dialysis but experience is limited.Linagliptin No dose adjustment required Experience in patients with end-stage renal disease or on dialysis is limited.Use with caution in these patients.Saxagliptin No dose adjustment Lower dose 2.5 mg once daily (<50 mL/min) Use alternative agent.Should not be used in patients on dialysis.Sitagliptin No dose adjustment Lower dose 50 mg daily (30-49 mL/min) Use lowest dose 25 mg daily Individualizing drug therapy in patients with diabetes and chronic kidney disease Lori MacCallum, BScPhm, PharmD, RPh, CDE 5 steps for ensuring optimal drug dosing in patients with chronic kidney disease 1 1.Estimate renal function using creatinine clearance or eGFR 2. Review ALL medications a patient is taking.It is important to look for:• Medications that are renally eliminated and/or have metabolites that are renally eliminated.• Medications with a potential for increased risk of adverse effects (e.g., hyperkalemia with angiotensin-converting enzyme [ACE] inhibitors and angiotensin II receptor blockers [ARB] and other medications that raise potassium).• Medications with a potential for drug interactions.• Medications where there may be decreased therapeutic response (e.g., thiazide diuretics in CKD stages 4 and 5).• Medications with the potential for nephrotoxicity (e.g., nonsteroidal anti-inflammatory drugs [NSAIDs]).3. Calculate an individualized dosing regimen based on the patient's treatment goals and degree of kidney function.In some cases, alternative therapy may be required.4. Monitor for efficacy and toxicity and adjust the new regimen as needed based on the patient's response.5. Advise all patients about the sick day medication list. 2 Dosing considerations for antihyperglycemics in chronic kidney disease 3-8 Practice tOOL 2 4 6 C P J / R P C • S e P t e m b e r / O c t O b e r 2 0 1 5 • V O L 1 4 8 , N O 5

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.022
GPT teacher head0.245
Teacher spread0.222 · 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
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

Citations1
Published2015
Admission routes2
Has abstractyes

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