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Record W22389795 · doi:10.5402/2011/463124

Three to tango: Prenegotiation and mediation in the reestablishment of Anglo-Argentine diplomatic relations (1983--1990)

2003· article· en· W22389795 on OpenAlexaboutno aff
Alejandro L. Corbacho

Bibliographic record

VenueISRN Rheumatology · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical and Social Dynamics in Chile and Latin America
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceMediationDiplomacyLawPolitics

Abstract

fetched live from OpenAlex

Objectives. Polymyositis (PM) and dermatomyositis (DM) are characterized by impaired muscle function with a majority of patients developing sustained disability. The aim of this study was to evaluate the patient's individual priorities (patient preference) of disabilities most important to improve in PM/DM using the MacMaster Toronto Arthritis Patient Preference Disability Questionnaire (MACTAR), to correlate the MACTAR to myositis outcomes and to evaluate its test-retest reliability. Methods. Twenty-eight patients with PM/DM performed recommended outcomes as well as the MACTAR, which was performed twice with one week apart. Results. Sexual activity, walking, biking, social activities, and sleep constituted the predominating disabilities. Seventy-two and 33% of the identified disabilities were not covered by items of the Health Assessment Questionnaire and the Myositis Activities Profile. Correlations between the MACTAR and health-related quality of life measures were r(s) = -0.67-0.73, correlations with measures of activities of daily living and participation in society were r(s) = 0.51-0.60 with lower correlations for other outcomes. Intraclass correlation (ICC) and weighted Kappa (K(w)) coefficients were 0.83 and 0.68, respectively, for test-retest reliability of the MACTAR. Conclusions. The MACTAR interview had promising measurement properties and identified patient preference disabilities in PM/DM that were not covered by recommended outcomes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.015
GPT teacher head0.296
Teacher spread0.281 · 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 designTheoretical or conceptual
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
Published2003
Admission routes1
Has abstractyes

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