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Record W2044464329 · doi:10.1155/2014/828965

Remission from Depression among Adults with Arthritis: A 12-Year Followup of a Population-Based Study

2014· article· en· W2044464329 on OpenAlexafffund
Esme Fuller‐Thomson, Marla Battiston, Tahany M. Gadalla, Yael Shaked, Ferrah Raza

Bibliographic record

VenueDepression Research and Treatment · 2014
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsMedicineDepression (economics)ArthritisPopulationPsychological interventionPhysical therapyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Individuals with arthritis are vulnerable to depression. In this study, we calculated time to remission from depression in a representative community-based sample of depressed Canadians with arthritis who were followed for 12 years. We conducted secondary analysis of a longitudinal panel study, the National Population Health Survey, which was begun in 1994/95 and has included biennial assessment of depression since that time. Our analysis focused on a total of 216 respondents with arthritis who were depressed at baseline. The mean time to remission from depression was calculated using the Kaplan-Meier procedure and compared across categories of each of the potential predictors. The percentage of those no longer screening positive for depression was calculated at two years after baseline. At two years after baseline, 71% of the sample had achieved remission from depression. Time to remission was significantly longer for those depressed adults who were under the age of 55, those who reported more chronic pain at baseline, those with comorbid migraine, and those who experienced childhood physical abuse or parental addictions. These findings highlight the importance of screening for these factors to improve the targeting of interventions to depressed patients with arthritis.

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 score0.079
Threshold uncertainty score0.608

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 head0.029
GPT teacher head0.326
Teacher spread0.297 · 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

Citations1
Published2014
Admission routes2
Has abstractyes

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