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Record W4388705647 · doi:10.1097/yic.0000000000000521

The impact of mild behavioral impairment on the prognosis of geriatric depression: preliminary results

2023· article· en· W4388705647 on OpenAlexafffund
Camilla Elefante, Giulio Emilio Brancati, Gabriele Pistolesi, Salvatore Amadori, S. Torrigiani, Filippo Baldacci, Roberto Ceravolo, Zahinoor Ismail, Lorenzo Lattanzi, Giulio Perugi

Bibliographic record

VenueInternational Clinical Psychopharmacology · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Calgary
FundersNational Institute on AgingCanadian Institutes of Health ResearchNeuraxpharmH. Lundbeck A/SFondation Brain CanadaJohns Hopkins UniversityBiogenSanofiConsortium canadien en neurodégénérescence associée au vieillissementAlzheimer's Drug Discovery Foundation
KeywordsDepression (economics)MedicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

Our study aimed to examine how the presence of Mild Behavioral Impairment (MBI) symptoms influenced the outcome of late-life depression (LLD). Twenty-nine elderly (≥ 60 years) depressive patients, including eleven (37.9%) with MBI, were recruited and followed-up on average for 33.41 ± 8.24 weeks. Psychiatric symptoms severity and global functioning were assessed, respectively, using the Brief Psychiatric Rating Scale (BPRS) and the Global Assessment of Functioning (GAF) scale. BPRS total score significantly decreased from baseline to follow-up ( P < 0.001, d = 1.33). The presence of MBI had no significant effect on mood and cognitive symptoms improvement. On the contrary, while a significant increase in GAF score was observed in patients without MBI ( P = 0.001, d = 1.01), no significant improvement of global functioning was detected in those with MBI ( P = 0.154, d = 0.34) after 6-month follow-up. The presence of MBI in patients with LLD may negatively affect long-term outcome, slowing or preventing functional improvement.

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)0.002
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 score0.324
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.102
GPT teacher head0.529
Teacher spread0.427 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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