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Record W4403613839 · doi:10.1097/wnn.0000000000000380

Psychotropic Polypharmacy Leading to Reversible Dementia: A Case Report

2024· article· en· W4403613839 on OpenAlexaff
Durjoy Lahiri, Bruna Seixas Lima, Carlos Roncero, Kathryn A. Stokes, Swayang Sudha Panda, Howard Chertkow

Bibliographic record

VenueCognitive and Behavioral Neurology · 2024
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsBaycrest HospitalQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsPolypharmacyMedicineDementiaPsychiatryPediatricsIntensive care medicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Psychotropic polypharmacy presents a diagnostic challenge that may be further complicated by inadequate medication history and underappreciation of the cognitive effects of such polypharmacy. Here we present the case of a 57-year-old man who presented to our memory clinic with progressive cognitive decline and a prior neuropsychological evaluation supporting the diagnosis of a neurodegenerative disorder. He was taking multiple psychotropic medications at the time, but the exact dosages were unclear due to a lack of collateral history. He was also taking prescribed opioids and a combination of buprenorphine and naloxone for pain relief, again with unclear dosages at the time of presentation. Brain imaging and cerebrospinal spinal fluid biomarker testing were negative for Alzheimer pathophysiologic processes. Months later, the patient was taken to the emergency room after an overdose caused by overuse of opioid medications. Once he was taken off all psychoactive medications, the patient's cognitive impairment completely reversed, and he became independent in activities of daily living. Psychotropic polypharmacy can have a myriad of cognitive manifestations which need to be better recognized by clinicians. Deprescription of such medications should be attempted whenever clinically appropriate.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.194
GPT teacher head0.482
Teacher spread0.288 · 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 designCase report
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

Citations2
Published2024
Admission routes1
Has abstractyes

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