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Record W4308332180 · doi:10.35680/2372-0247.1690

The importance of patient engagement in the management of giant cell arteritis

2022· article· en· W4308332180 on OpenAlexaff
Nikhil S. Patil, Maxwell J. Gelkopf, Santano L. Rodrigues, Arun Sundaram

Bibliographic record

VenuePatient Experience Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsWestern UniversityUniversity of TorontoMcMaster University
Fundersnot available
KeywordsGiant cell arteritisMedicineDiseaseGlucocorticoidArteritisPatient experienceIntensive care medicineComplicationDermatologySurgeryVasculitisInternal medicineHealth care

Abstract

fetched live from OpenAlex

Giant cell arteritis (GCA) is a vascular condition characterized by ocular, systemic and neurological symptoms. Visual loss is the dreaded complication of this condition, prompting expedient diagnosis and treatment. Treatment typically involves prolonged glucocorticoid therapy which is often accompanied by several undesired side effects. In this report, we document one patient’s experience with GCA, its treatment, and relapse. Particularly, the patient highlights the difficulties and successes he had with the disease and its treatment. We also document the physician perspective on this disease and its treatment, specifically discussing the significance of prolonged treatment with glucocorticoids. We comment on the importance of patient experience and engagement in the setting of GCA and its management, particularly focusing on our recommendations of patient education over multiple visits and individualizing glucocorticoid treatment. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.012
GPT teacher head0.250
Teacher spread0.239 · 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 designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

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