Classification of musical hallucinations and the characters along with neural-molecular mechanisms of musical hallucinations associated with psychiatric disorders
Bibliographic record
Abstract
BACKGROUND Musical hallucinations (MH) involve the false perception of music in the absence of external stimuli which links with different etiologies. The pathomechanisms of MH encompass various conditions. The etiological classification of MH is of particular importance and offers valuable insights to understand MH, and further to develop the effective treatment of MH. Over the recent decades, more MH cases have been reported, revealing newly identified medical and psychiatric causes of MH. Functional imaging studies reveal that MH activates a wide array of brain regions. An up-to-date analysis on MH, especially on MH comorbid psychiatric conditions is warranted. AIM To propose a new classification of MH; to study the age and gender differences of MH in mental disorders; and neuropathology of MH. METHODS Literatures searches were conducted using keywords such as “music hallucination,” “music hallucination and mental illness,” “music hallucination and gender difference,” and “music hallucination and psychiatric disease” in the databases of PubMed, Google Scholar, and Web of Science. MH cases were collected and categorized based on their etiologies. The t -test and ANOVA were employed (P < 0.05) to compare the age differences of MH different etiological groups. Function neuroimaging studies of neural networks regulating MH and their possible molecular mechanisms were discussed. RESULTS Among the 357 yielded publications, 294 MH cases were collected. The average age of MH cases was 67.9 years, with a predominance of females (66.8% females vs 33.2% males). MH was classified into eight groups based on their etiological mechanisms. Statistical analysis of MH cases indicates varying associations with psychiatric diagnoses. CONCLUSION We carried out a more comprehensive review of MH studies. For the first time according to our knowledge, we demonstrated the psychiatric conditions linked and/or associated with MH from statistical, biological and molecular point of view.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".