They are not real patients
Bibliographic record
Abstract
Introduction Cognitive depressive disorder (or depressive pseudodementia) is a condition defined by functional impairment, similar to dementias or other neurodegenerative disorders, in the context of psychiatric patients. It is important to consider a differential diagnosis in patients with cognitive impairment. Objectives Presentation of a clinical case of a patient with depression with psychotic symptoms who presents cognitive impairment. Methods Bibliographic review of the differential diagnosis between cognitive depressive disorder and real dementia by searching for articles in PubMed. Results We present a 51-year-old woman, previously diagnosed with adjustment disorder (with mixed anxiety and depressed mood) and unspecific anxiety disorder, who was admitted to the hospital due to delusional ideation of harm and Capgras syndrome, ensuring that her relatives had been replaced and the rest of the patients were not real patients, but actors who conspired against her. The MRI (Magnetic Resonance Imaging) was strictly normal (tumors or acute injuries as stroke or hemorrhage were discarded), and a MoCA (Montreal Cognitive Assesment) test was performed to screen any cognitive impairments (obtaining a score of 19/30, with language fluency and abstraction particularly affected). It would be convenient to repeat the test when this episode and the psychotic symptoms are resolved or improved. Conclusions 1. Some patients may have cognitive impairment in the context of a mood disorder. 2. A differential diagnosis and follow-up of these patients should be performed to assess prognosis, reversibility and treatment. 3. Depressive cognitive impairment may precede the development and establishment of a dementia or neurodegenerative picture. Disclosure No significant relationships.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".