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Record W2065594439 · doi:10.1159/000212983

Treatment Considerations in the Elderly Rheumatic Patient

2009· review· en· W2065594439 on OpenAlexaff
Nicholas Bellamy

Bibliographic record

VenueGerontology · 2009
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsVictoria HospitalWestern University
Fundersnot available
KeywordsMedicineIntensive care medicineAdverse effectDiseaseModalitiesDrugPopulationPharmacotherapyAntirheumatic drugsPhysical therapyAntirheumatic AgentsInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

The elderly represent a large subset of the rheumatic population, some of whom have experienced musculoskeletal disease since early life or middle age, whereas others are affected for the first time in their later years. They may be afflicted with a variety of musculoskeletal disorders, some of which occur almost exclusively in elderly individuals. Advancing age may be accompanied by failure of the musculoskeletal system and other major organs. As a result, elderly patients frequently receive concurrent treatment with several pharmacologically active compounds, which increases the potential for significant adverse drug-drug interactions. In addition, the elderly may be less tolerant of certain classes of compounds, including some antirheumatic drugs, necessitating careful drug selection and patient monitoring. Diagnostic and therapeutic decision making may be impeded by the patient's inability to recall completely and accurately important historical details, particularly those relating to drug therapy. Treatment objectives may be compromised further by poor compliance, and adequate follow-up made more difficult by the patient's lack of mobility and declining independence. Successful management of the elderly rheumatic patient, therefore, requires an accurate clinical assessment, comprehensive evaluation of major organ functioning, identification of potential drug-drug interactions, appropriate selection of anti-rheumatic drugs and other treatment modalities, effective doctor-patient communication, and careful monitoring for both beneficial and adverse responses to therapy.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.094
GPT teacher head0.379
Teacher spread0.285 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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