Brain-Derived Neurotrophic Factor and Suicide in Schizophrenia: Critical Role of Neuroprotective Mechanisms as an Emerging Hypothesis
Bibliographic record
Abstract
Suicide is a common occurrence in psychiatric disorders and is a cause of increased healthcare utilization worldwide. Schizophrenia is one of the most common psychiatric disorders worldwide and posited to be seen in 1% of the population worldwide. Suicide is a common occurrence in schizophrenia with 25%-30% patients with schizophrenia attempting suicide and 8%-10% completing it. There is a need for valid biological markers to help clinicians identify patients with schizophrenia that may be at a risk of suicide and thus help in them receiving better care and interventions at the earliest even before a suicide attempt occurring. There are clear neurobiological changes at a genetic, neuroimaging, and neurochemical level that occurs in patients with schizophrenia that attempt suicide. There is a new theory that postulates neuronal plasticity and neuroprotection to have a role in the biological changes that ensue when suicidal thoughts and feelings occur in patients with schizophrenia. Neurotrophic growth factors like brain-derived neurotrophic factor (BDNF) have been documented to play a role in the protection of neurons and in the prevention of neurobiological changes that may lead to suicide both in schizophrenia and depression. The present paper presents a commentary that looks at the role of BDNF as a protective factor and neurobiological marker for suicide in schizophrenia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".