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Record W2059589565 · doi:10.1089/109662101300051906

Depression with Psychomotor Retardation: Diagnostic Challenges and the Use of Psychostimulants

2001· article· en· W2059589565 on OpenAlexaff
José Pereira, Éduardo Bruera

Bibliographic record

VenueJournal of Palliative Medicine · 2001
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsGrey Nuns Community Hospital
Fundersnot available
KeywordsPsychomotor retardationMedicinePsychomotor agitationDepression (economics)Psychomotor learningMethylphenidatePalliative careMoodPsychiatryIntensive care medicineAlternative medicineCognitionAttention deficit hyperactivity disorderNursing

Abstract

fetched live from OpenAlex

A patient with advanced pancreatic cancer is presented to demonstrate the clinical challenge of diagnosing depression in palliative care. The conundrum related to the relative roles of somatic and psychological symptoms in screening or diagnosing depression in these patients is illustrated and discussed. There is no clear consensus on how to apply diagnostic criteria for diagnosing depression in these patients. Although an approach that focuses on the psychological symptoms is often suggested, it appears that somatic criteria cannot be entirely excluded. The case also highlights the use of methylphenidate to treat palliative care patients. As compared to traditional antidepressants that may take as long as 6-8 weeks to have a full effect, they offer the advantage of onset of action within a few days. This is especially helpful in patients with limited life expectancies. They appear to be particularly advantageous where psychomotor retardation is a main feature of the depression. The patient discussed demonstrated an observed and self-reported improvement of mood and psychomotor retardation following the initiation of psychostimulant treatment. Larger, controlled trials, using specified criteria to diagnose depression, are warranted to elucidate the role of psychostimulants in treating depression in palliative care patients.

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)0.004
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 score0.094
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.129
GPT teacher head0.374
Teacher spread0.246 · 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

Citations23
Published2001
Admission routes1
Has abstractyes

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