Improving Surgical Quality for Patients With Mental Illnesses
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify disparities in care for surgical patients with preexisting mental health diagnoses. SUMMARY BACKGROUND DATA: Mental illness affects approximately 6.7 million Canadians. For them, stigma, comorbid disorders, and sequelae of psychiatric diagnoses can be barriers to equitable health care. The goal of this review is to define inequities in surgical care for patients with preexisting mental illness. METHODS: We searched OVID Medline, Pubmed, EMBASE, and the Cochrane review files using a combination of search terms using a PICO (population, intervention, comparison, outcome) model focusing on surgical care for patients with mental illness. RESULTS: The literature on mental illness in surgical patients focused primarily on preoperative and postoperative disparities in surgical care between patients with and without a diagnosis of mental illness. Preoperatively, patients were 7.5% to 40% less likely to be deemed surgical candidates, were less likely to receive testing, and were more likely to present at later stages of their disease or have delayed surgical care. Similar themes arose in the postoperative period: patients with mental illness were more likely to require ICU admission, were up to 3 times more likely to have a prolonged length of hospital stay, had a 14% to 270% increased likelihood of having postoperative complications, and had significantly higher health care costs. CONCLUSIONS: Surgical patients with preexisting psychiatric diagnoses have a propensity for worse perioperative outcomes compared to patients without reported mental illness. Taking a thorough psychiatric history can potentially help surgical teams address disparities in access to care as well as anticipate and prevent adverse outcomes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".