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Quality of life issues and occupational performance of persons with epilepsy

2012· article· en· W2086848249 on OpenAlexaboutno aff
Renato Nickel, Carlos Eduardo Silvado, Francisco Manoel Branco Germiniani, Luciano de Paola, Nicolle Lucena da Silveira, Joana Rostirolla Batista de Souza, Cassiano Robert, Andressa Pereira Lima, Lauren Machado Pinto

Bibliographic record

VenueArquivos de Neuro-Psiquiatria · 2012
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsWorryEpilepsyQuality of life (healthcare)Affect (linguistics)PsychologyActivities of daily livingIntervention (counseling)MedicinePerceptionClinical psychologyPhysical therapyPsychiatryNursingAnxiety

Abstract

fetched live from OpenAlex

Epilepsy causes restrictions in the performance of various daily activities. The aiming of this study was to investigate whether these restrictions affect the perceived quality of life. The assessments Quality of Life in Epilepsy-31 (QOLIE-31) and Canadian Occupational Performance Measure (COPM) were applied in a sample that consisted of a single group of 34 subjects with at least two years of uncontrolled seizures. The results indicated that the most affected domains of QOLIE-31 were seizure worry, 29.77 (±21.72), and effects of drugs, 49.75 (±28.58), and for the COPM, the average of performance and satisfaction were respectively 3.10 (±3.07) and 4.45 (±3.29), and performance limitations most frequently cited were maintain employment (18), left home alone (15) and courses (15). The application of the Spearman correlation coefficient showed that the three main performance limitations posed by the COPM, especially regarding the level of satisfaction, influence the perception of quality of life. Thus, occupational performance proves to be an important area of intervention with subjects with epilepsy.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.426

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 head0.047
GPT teacher head0.348
Teacher spread0.300 · 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 designObservational
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

Citations45
Published2012
Admission routes1
Has abstractyes

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