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Record W4403107085 · doi:10.4140/tcp.n.2024.350

Geriatric Pharmacotherapy Case Series: Medications for Diabetes—A Focus on Secondary Stroke Prevention

2024· review· en· W4403107085 on OpenAlexaff
Sabrina Warren, Shayla McKee, Erin Yakiwchuk

Bibliographic record

VenueThe Senior Care Pharmacist · 2024
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineDiabetes mellitusStroke (engine)GlycemicIntensive care medicinePharmacotherapyType 2 diabetesDiseaseRegimenDiabetes managementClinical trialSurgeryInternal medicine

Abstract

fetched live from OpenAlex

This report addresses evidence for efficacy of diabetes medications with a focus on stroke risk reduction. The cardiovascular benefits of SGLT-2 inhibitors and GLP-1 receptor agonists have been well-established; however, clinical trials to date have examined composite cardiovascular endpoints that include, but do not specifically focus on, stroke. The purpose of this case review is to examine the evidence for the various diabetes medications in reducing the risk for stroke. This literature review was inspired by a patient seen in a geriatric day hospital program with diabetes and a history of multiple strokes. Our goal was to select a diabetes management regimen that would provide both glycemic control and stroke risk reduction. As diabetes and cerebrovascular disease commonly coexist and are important contributors to morbidity and mortality in older individuals, appropriate management must incorporate both current evidence as well as consideration for patient-specific factors that may influence the treatment plan. This patient case illustrates the importance of both.

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)0.000
metaresearch head score (Gemma)0.003
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: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.046
GPT teacher head0.390
Teacher spread0.344 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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