Long-Term Outcome of Leucotomy On Behaviour of People With Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Prefrontal leucotomy was widely used from the late 1930s to early 1950s as a treatment for disorders involving obsessive agitation. Comparatively few studies of the enduring behavioural effects of such surgery exist, while data on mortality and cognition have been better reported. AIMS: We contrast the psychosocial functioning of older individuals with schizophrenia who had undergone prefrontal leucotomy with two groups of their peers who had not undergone such surgery. METHOD: A total of 87 individuals (one female) with a mean age of 70.3 years (SD = 6.84) were evaluated twice 25 months apart using a standardized rating scale. Twenty of the residents, all with schizophrenia, had undergone prefrontal leucotomy approximately 45 years previously. All diagnoses of schizophrenia were confirmed by multiple psychiatrists using DSM-III criteria at the time of the ratings, which were completed by two care staff who knew the residents well. RESULTS: Repeated measures comparisons with schizophrenia and non-schizophrenia patient groups showed no significant differences between the leucotomy and unoperated comparison groups on four of the five Multidimensional Observation Scale for Elderly Subjects (MOSES) scales. CONCLUSIONS: These results are consistent with reports of compromised function among individuals who had undergone leucotomy and contrast with some reports of positive changes in behaviour.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".