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Record W1589340942 · doi:10.1139/jpn.0714

Onset of confusion in the context of late-life depression

2007· article· en· W1589340942 on OpenAlexaffvenue
Benoit H. Mulsant

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2007
Typearticle
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsVenlafaxineFeelingConfusionContext (archaeology)Depression (economics)MoodPsychiatryMedicineHyponatremiaPediatricsPsychologyAntidepressantInternal medicineHistoryPsychoanalysisAnxietySocial psychology

Abstract

fetched live from OpenAlex

A 70-year-old woman with a long history of mood disorder presented to her psychiatrist complaining of increasing confusion. She had a history of recurrent depression since her mid-thirties and had been on and off various antidepressant drugs. She felt they had not helped her, even though she had clearly improved on several of the medications, including venlafaxine. A couple of months earlier, she was feeling worse and was restarted on venlafaxine, 75 mg per day. After a couple of weeks, venlafaxine was increased to 150 mg per day. She reported that she had been feeling better since restarting venlafaxine: she rated her mood as 1 out of 10 (0 = worst, 10 = best) before she restarted the medication and 5 out of 10 since then. However, she felt confused. She could not pinpoint the onset of her confusion but thought it had started about 3 months earlier, when she felt severely depressed. She reported forgetting appointments, to the extent that her husband had to keep her schedule (she forgot to call her son for his birthday, missed turns on familiar roads and got lost in familiar neighbourhoods). Her venlafaxine was decreased to 75 mg a day, but her confusion persisted. The following week, she ran a red light and was involved in a motor vehicle accident. She was admitted to the hospital and, on admission, her serum sodium was found to be 128 mEq/L. Case-controlled and retrospective studies have reported an incidence of hyponatremia between 10% and 40% in older patients treated with serotonergic antidepressant drugs. In one study, 10 of 14 (71%) older patients taking venlafaxine were found to be hyponatremic (Kirby and others, Int J Geriatr Psychiatry 2002;17:231-7). In the only published prospective study (Fabian and others, Arch Intern Med 2004;164:327-32), 9 of 75 (12%) older patients developed hyponatremia within 1 to 14 days after initiation of paroxetine. This underestimates the incidence in clinical settings, because the study design included monitoring and prevention of hyponatremia. Risk factors for hyponatremia induced by serotonergic antidepressant drugs include older age, female sex, low weight, intake of diuretics and lower baseline sodium levels (Movig and others, Br J Clin Pharmacol 2002;53:363-9). Hyponatremia does not appear to be related to the dose or plasma level of the serotonergic antidepressant. It is caused by an inappropriate secretion of ADH, resulting from enhanced serotonergic tone, as experimentally demonstrated in rats. However, even in the presence of elevated ADH, hyponatremia will not occur unless fluid intake is also increased (e.g., when managing constipation or a urinary infection). Most clinicians do not realize how commonly serotonergic antidepressant drugs induce hyponatremia, because associated symptoms are nonspecific and are interpreted as either symptoms of depression (e.g., fatigue, anorexia, confusion) or medication side effects (e.g., fatigue, nausea). In the prospective study by Fabian and colleagues, only 1 of the 9 patients with hyponatremia became clearly symptomatic. In most patients, hyponatremia is mild and transient, but in some cases, if undetected, it can be persistent and progress to seizure, coma or death. Thus, it is prudent to check sodium levels at baseline and 1–2 weeks after initiating a serotonergic antidepressant in older patients with additional risk factors or who receive other medications associated with ADH secretion (e.g., thiazide diuretics, antipsychotic drugs, or non-steroidal anti-inflammatory drugs). If mild hyponatremia develops, it can be managed with fluid restriction (i.e., 800–1000 mL per day) and close monitoring. In the absence of systematic screening, clinicians should maintain a high index of suspicion for hyponatremia and promptly check sodium if a patient becomes lethargic or confused.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.107

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.295
Teacher spread0.279 · 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

Citations4
Published2007
Admission routes2
Has abstractyes

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