Mirrored Self-Misidentification Syndrome: A Systematic Review of Cases
Bibliographic record
Abstract
ObjectiveMirrored self-misidentification syndrome (MSMS) is a rare form of delusional misidentification syndrome characterized by the inability to recognize one's own reflection. We conducted a systematic review aiming to describe the epidemiology, clinical presentation, and management of individuals with MSMS.MethodsA comprehensive literature search was performed using original case reports/series on patients with MSMS. Univariate analyses were performed to assess patient demographics, clinical, paraclinical, and treatment-related characteristics. The methodological quality of included articles was evaluated using a standardized tool.ResultsOf 76 articles screened, 28 were included, with 36 patients analyzed. Median age was 77.0 (interquartile range: 72.0, 80.0) years; most patients were female (60.7%). Over half of the cases had a diagnosis of dementia, mostly Alzheimer's disease (50.0%), Lewy Body Disease (20.0%), and vascular dementia (10.0%), while the other diagnosis included stroke (3.3%), schizophrenia (3.3%), schizoaffective disorder (3.3%), and rabies (3.3%). Initial clinical manifestations included psychiatric symptoms (66.7%) and cognitive decline (70.0%). Brain magnetic resonance (MRI) was reported in 31 cases, with 14 cases (45.1%) showing right hemisphere dysfunction. Pharmacological interventions were effective for twelve cases (48.0%), and non-pharmacological interventions such as covering mirror were effective for 8 cases (32.0%). Most included articles (64.3%) were evaluated to be at low risk of bias.ConclusionsMSMS are rare conditions that mostly present in patients with dementia. Despite the varied clinical presentations, frontal and right hemisphere dysfunctions appear to play a role in the pathophysiology of MSMS, adding to the evidence supporting "a neuroanatomy of the self" in the non-dominant hemisphere.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".