MétaCan
Menu
Back to cohort
Record W3173148156 · doi:10.1590/1518-8345.4737.3463

Pain in the bipolar disorder: prevalence, characteristics and relationship with suicide risk

2021· article· en· W3173148156 on OpenAlexaboutno aff
Ana Carolina Ferreira Rosa, Eliseth Ribeiro Leão

Bibliographic record

VenueRevista Latino-Americana de Enfermagem · 2021
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingBipolar disorderPsychiatrySuicidal ideationObservational studyClinical psychologySuicide RiskMedicineDepression (economics)Chronic painPsychologySuicide preventionPoison controlCognitionInternal medicineMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: to know the prevalence and characteristics of pain, to verify how pain management has been carried out by the health services, and to correlate suicide risk with pain intensity in patients with bipolar disorder. METHOD: an observational study with a quantitative approach. The study included people with bipolar disorder assessed by the McGill-Reduced Pain Questionnaire, Body Diagram, Visual Numerical Scale, and the Suicidal Ideation Scale (Beck). RESULTS: the sample of 60 participants was mainly composed of women with a mean age of 40 years old and a mean psychiatric treatment time of approximately 13 years. Of these, 83% reported feeling pain at the time of the interview. Half of the participants indicated that pain interferes with routine and 80% did not receive care in health institutions. The main descriptors that qualify the painful experience were as follows: painful, heavy and sensitive for the sensory descriptors, tiring and punishing in the affective category. Suicide attempt was reported by 57% of the participants. There was a correlation between suicide risk and pain intensity. CONCLUSION: pain presented a high prevalence. Suicide risk was identified in more than half of the participants. Pain intensity showed a significant correlation with suicide risk.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.276
Teacher spread0.257 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueRevista Latino-Americana de EnfermagemSame topicBipolar Disorder and TreatmentFrench-language works237,207