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Record W2030581467 · doi:10.1017/s1041610200006578

Feasibility and Effectiveness of Treatments for Depression in Elderly Medical Inpatients: A Systematic Review

2000· review· en· W2030581467 on OpenAlexaff
Martín G. Cole, L. Michel Élie, Jane McCusker, François Bellavance, Asmaa Mansour

Bibliographic record

VenueInternational Psychogeriatrics · 2000
Typereview
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcGill UniversityHEC MontréalSt Mary's Hospital Centre
Fundersnot available
KeywordsFluoxetineDiscontinuationDepression (economics)MedicinePsychiatryMEDLINELate life depressionAntidepressantSertralineSerotonin reuptake inhibitorInternal medicineSerotoninAnxiety

Abstract

fetched live from OpenAlex

To determine the feasibility and effectiveness of treatments for depressed elderly medical inpatients, MEDLINE was searched for potentially relevant articles published from January 1987 to August 1997, using the keywords "depression or depressive disorder" (exploded) and "aged." The bibliographies of relevant articles were searched for additional references. Fifteen reports met the following inclusion criteria: (a) published in English or French; (b) minimum age criterion of 55 and over or mean age 65 and over; (c) subjects admitted to the medical service of an acute care hospital; (d) used accepted criteria for depression; (e) examined treatment(s) for depression; and (f) reported outcomes as a depression diagnosis and/or symptom level. Information was abstracted independently from each article by two reviewers, tabulated, and compared. The limited evidence suggests contraindications to treatment in 38% to 87% of subjects who received a heterocyclic antidepressant compared to 4% of subjects who received the selective serotonin reuptake inhibitor (SSRI) fluoxetine; rates of discontinuation and study completion were similar for heterocyclics, the SSRIs, and psychostimulants. All of the treatments (including social support/psychotherapy) appeared to be at least modestly effective in the short term.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.306
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.062
GPT teacher head0.475
Teacher spread0.413 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations29
Published2000
Admission routes1
Has abstractyes

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