Factors Predicting the Presence of Impaired Insight in Liaison Psychiatric Patients Presenting to the Emergency Room
Bibliographic record
Abstract
Objective To examine the factors that can predict the likelihood that a patient presenting to the Emergency Room will have impaired insight. Methods Twenty-two independent demographic and clinical factors contained on data assessment tools for 337 patients assessed by the crisis team in the ER over 6 months were compiled and analysed using SPSS Version 20 with univariate analyses and logistic regression. Results Only four (employment status, history of self-harm, reason for presenting to the ER and psychotic symptoms present) of the twelfth-predictor variables on univariate analysis made unique statistically significant contributions to a logistic regression model. Patients who were unemployed or had a history of self-harm were about two and three times respectively more likely to have impaired insight compared with those who were employed or had no history of self-harm, controlling for other factors in the model. Patients who had psychotic symptoms on mental state examination were about six times more likely to have impaired insight compared to those who did not have psychotic symptoms, controlling for other factors in the model. Patients presenting to the ER with psychotic symptoms or drug/alcohol problems as chief complaints were 25 times and four times respectively more likely to present with impaired insight compared to patients presenting with a medical complaint, controlling for other factors in the model. Conclusion Patients presenting to the ER with a psychotic symptom and those with psychotic symptoms on metal state examination as well as those presenting with drug/alcohol problems are candidates for an insight-oriented psychotherapy.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".