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Record W2067605344 · doi:10.3899/jrheum.110899

Quality of Life in Patients with Immune-Mediated Inflammatory Diseases

2011· review· en· W2067605344 on OpenAlexafffundvenue
Anthony S. Russell, Wayne Gulliver, E. Jan Irvine, Salvatore Albani, Jan Dutz

Bibliographic record

VenueJournal of Rheumatology Supplement · 2011
Typereview
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsUniversity of Alberta
FundersAbbott CanadaMichael Smith Health Research BCChild and Family Research InstitutePfizerAmgen
KeywordsMedicineQuality of life (healthcare)DiseaseClinical trialPhysical therapyHealth related quality of lifeIntensive care medicineInclusion (mineral)Internal medicineNursing

Abstract

fetched live from OpenAlex

There is no doubt that patients with immune-mediated inflammatory diseases (IMID) have a significantly impaired quality of life (QOL). Pain and disability often leave these patients helpless and frustrated. The recognition that addressing physical and psychological functioning plays a significant role in an overall treatment approach led to the inclusion of QOL measures as secondary outcomes in clinical trials with IMID patients. To that end, both generic and disease-specific instruments have been utilized. Measurement of health-related QOL (HRQOL) and patient-reported outcomes (PRO) in a controlled manner allows for better understanding of the correlation between different aspects of disease activity and QOL. In addition, the effects of different therapeutic options on HRQOL-related outcomes can be further evaluated. This 3-part section describes key QOL-related complaints of patients with IMID affecting joints, skin, or gut. An overview of the strengths and weaknesses of various commonly used HRQOL instruments is provided. Finally, the influence of anti-tumor necrosis factor-α agents on HRQOL outcomes, as assessed in recent clinical trials, is highlighted.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.275
Teacher spread0.250 · 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

Citations35
Published2011
Admission routes3
Has abstractyes

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