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Record W17429133 · doi:10.1159/000468412

Drug-Induced Mood Disorders

2017· review· en· W17429133 on OpenAlex
J. Ananth, A.M. Ghadirian

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Pharmacopsychiatry · 2017
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsDepression (economics)MedicineDrugMoodPsychiatryMetronidazoleIntensive care medicinePhysostigmineAntibioticsInternal medicineCholinergic

Abstract

fetched live from OpenAlex

Various drugs including antihypertensives, anxiolytics, antibiotics, antidepressants, corticosteroids, choline, indomethacin, levodopa, metronidazole, neuroleptics, oral contraceptives, sulphonamides and physostigmine have been reported to produce depression as a side effect. Clinically, these drug-induced depressions may go unnoticed and thus create therapeutic problems. Although causal relationship is difficult to establish, depression occurring during the course of drug treatment needs an evaluation of all the medications that the patient has been receiving. We believe that postpsychotic depressions include three types of depression: pendular depression--primarily disease related; chronic depression--primarily environment related, and amine-depletion depression--drug related. Thus, drug-induced depressions constitute a subgroup of postpsychotic depression. Clinically, it is essential to carefully monitor patients receiving drugs known to produce depression. Thus, prompt recognition of the drug-induced depressions may assist in initiating proper therapeutic measures.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.089
GPT teacher head0.467
Teacher spread0.377 · 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