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Dor, cinesiofobia e qualidade de Vida em pacientes com lombalgia crônica e depressão

2013· article· pt· W2037586840 on OpenAlexaboutno aff
Rogério Sarmento Antunes, Bárbara Gazolla de Macedo, Tammy da Silva Amaral, Henrique de Alencar Gomes, Leani Souza Máximo Pereira, Fábio Lopes Rocha

Bibliographic record

VenueActa Ortopédica Brasileira · 2013
Typearticle
Languagept
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)Quality of life (healthcare)VitalityBeck Depression InventoryMedicinePhysical therapyCross-sectional studyChronic painMental healthMcGill Pain QuestionnairePain catastrophizingPsychiatryVisual analogue scaleAnxiety

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the characteristics of pain, kinesiophobia and quality of life in patients with chronic low back pain and depression. METHODS: Cross-sectional study in which 193 individuals with chronic low back pain were included. The presence of depression was measured by the Beck Depression Inventory, using a cutoff validated by the Mini International Neuropsychiatric Interview. The intensity and quality of pain in the groups with and without depression were assessed by the McGill Questionnaire. The Tampa Scale for Kinesiophobia was applied to assess fear of movement. With respect to quality of life, the Medical Outcomes Study 36 was used. The statistical significance level was set at p <0.05. RESULTS: The prevalence of depression was 32.1%. The group with depression had worse scores in relation to pain, kinesiophobia and quality of life (physical functioning, rolephysical, bodily pain, general health, vitality, social functioning, role-emotional, and mental health. CONCLUSION: Patients with low back pain and depression had higher pain intensity, greater fear of movement and poorer quality of life. Level of Evidence III, Cross-sectional.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.287
Teacher spread0.271 · 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 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

Citations76
Published2013
Admission routes1
Has abstractyes

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