Bibliographic record
Abstract
This report presents the frequency at which physical conditions appear in patients with schizophrenia as well as those without any reported psychiatric disorders. The trends of this data set shows that specific physical disorders (i.e. cardiovascular disease) may present at a higher percentage in those with schizophrenia compared to those with no psychiatric disorder. Results are based on a dataset of registration information for 16,359 individuals with schizophrenia. The physical diagnosis frequencies were calculated for a 16 fiscal year period (1994-2010). A second group was generated based on physician records of those without any psychiatric disorder. The highest frequencies of co-morbid physical disorders in individuals with schizophrenia are sprains and strains of the lumbar and thoracic regions at 5.71% and 5.20% respectively. However, these also presented at >1% of physical illnesses in patients without psychiatric disorders. Cardiovascular disease appeared at a percentage of 2.83% physical disorders in patients with schizophrenia as opposed to 0.10% in those without any mental health disorder. Review of recent literature was conducted to find possible reasoning for the higher prevalence of cardiovascular disease in those with Schizophrenia. Findings from this report suggest a correlation between some physical disorders and schizophrenia. In the case of cardiovascular disease and consequently higher financial costs and mortality rates, this creates implications for more attentive treatment and preventive measures for such somatic disorders in those with 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".