Evolution in the Treatment of Psychiatric Disorders: From Psychosurgery to Psychopharmacology to Neuromodulation
Bibliographic record
Abstract
The treatment of psychiatric patients presents significant challenges to the clinical community, and a multidisciplinary approach to diagnosis and management is essential to facilitate optimal care. At times, these treatments have involved mystical, medical and surgical approaches, and developments in the field have sparked the interest of psychiatrists, psychologists, and neurosurgeons, among others. In particular, the neurosurgical treatment of psychiatric disorders, or “psychosurgery”, has held fascination throughout human history as a potential method of influencing behavior and consciousness. Early evidence of such procedures can be traced to prehistory, and interest flourished in the nineteenth and early twentieth century with greater insight into cerebral functional and anatomic localization. However, any discussion of psychosurgery invariably invokes controversy, as the widespread and indiscriminate use of the transorbital lobotomy in the mid-twentieth century resulted in profound ethical ramifications that persist to this day. The concurrent development of effective psychopharmacological treatments, including lithium and chlorpromazine virtually eliminated the need and desire for psychosurgical procedures, and accordingly the research and practice of psychosurgery was dormant, but not forgotten. The evolution in the treatment of psychiatric disorders from historical to modern practice is intimately connected with developments in behavioral neuroscience, neuroimaging, psychopharmacology, and neurosurgery. With the fall of ablative surgery came the rise of psychopharmacology, although the high rates of treatment resistance or failure necessitate alternative strategies. There has been a recent resurgence in interest for non-ablative surgery for psychiatric disorders, due in part to modern advances in functional and structural neuroimaging and neuromodulation technology. In particular, deep brain stimulation is a promising treatment paradigm with the potential to modulate abnormal pathways and networks implicated in psychiatric disease states. Although there is enthusiasm regarding this resurgence in surgical treatments, it is important to reflect on the scientific, social, and ethical considerations of this controversial field.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".