Bridging the mind and body: exploring venous thromboembolism in psychiatric inpatients
Bibliographic record
Abstract
Objective This study aimed to identify factors associated with venous thromboembolism (VTE) diagnosis in psychiatric inpatients in Saskatoon, Saskatchewan, Canada. Methods We conducted a retrospective case-control chart review of patients admitted to the Dube Centre for Mental Health from January 2007 to December 2021. Cases were individuals aged 18 years and older who received anticoagulation for VTE treatment. Controls were randomly selected, with case-to-control ratio 1:4, from patients with a discharge diagnosis not including VTE. Data were analyzed using descriptive analysis, univariate, followed by multivariable logistic regression analysis to identify factors associated with VTE diagnosis. Results A total of 32 VTE and 159 non-VTE patients were included. The mean age of VTE patients was 52 years (standard deviation [SD] = 19.7), 65.6% were female, and 65.6% had no previous VTE. Comorbidities including cancer (adjusted odds ratio [AOR] = 51.83; p = .004), cardiovascular conditions (AOR = 7.83; p = .01), and insomnia (AOR = 88.79; p = .01); psychiatric-specific interventions such as electroconvulsive therapy (AOR = 21.10; p < .001) and mechanical restraints (AOR = 12.65; p = .004); and acute medical diagnoses (AOR = 8.56; p = .01) were independently associated with developing VTE. Conclusions Psychiatric inpatients have unique factors that increase the likelihood of developing VTE. Further research with a larger sample size and multicenter design is needed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".