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Impact of the disease: acceptability, ceiling and floor effects and reliability of an instrument on heart failure

2013· article· en· W2144306143 on OpenAlexaff
Simey de Lima Lopes Rodrigues, Roberta Cunha Matheus Rodrigues, Thaís Moreira São‐João, Renata Bigatti Bellizzotti Pavan, Kátia Melissa Padilha, Maria Cecília Bueno Jayme Gallani

Bibliographic record

VenueRevista da Escola de Enfermagem da USP · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCronbach's alphaCeiling (cloud)Intraclass correlationCeiling effectInternal consistencyMedicineReliability (semiconductor)Physical therapyPsychometricsClinical psychology

Abstract

fetched live from OpenAlex

This study evaluated the acceptability, ceiling/floor effects, and the reliability of the instrument for measuring the Impact of the Disease on the Daily Life of Patients with Valvular Disease (IDCV) when applied to 135 patients with heart failure (HF). Acceptability was evaluated by the percentage of unanswered items and by the proportion of patients who responded to all items; the ceiling/floor effects by the percentage of patients who scored in the top of 10% best and worst results of the scale, respectively. Reliability was estimated by internal consistency (Cronbach's alpha coefficient) and stability of the measure (intraclass correlation coefficient - ICC). All patients responded to all items. Ceiling/floor effects evidenced were of moderate magnitude. The Cronbach's alpha was satisfactory for the majority of the domains and ICC> 0.90 in all the domains. The IDCV proved to be an easy to understand questionnaire, with evidence of reliability in patients with HF.

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)0.023
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.109
GPT teacher head0.398
Teacher spread0.289 · 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.

Study designObservational
DomainMethods
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

Citations37
Published2013
Admission routes1
Has abstractyes

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Same venueRevista da Escola de Enfermagem da USPSame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207