An overview of the rehabilitation and psychiatric diagnoses of patients referred to a psychiatry consult liaison service at an inpatient rehabilitation hospital
Bibliographic record
Abstract
BACKGROUND: Patients presenting for inpatient rehabilitation following injury or illness are commonly affected by comorbid psychiatric illness. Currently, little is known about the utilization of a psychiatry consult service in an inpatient rehabilitation hospital. OBJECTIVE: To identify which rehabilitation patient populations most frequently received psychiatric consultation and recognize the most common psychiatric comorbidities after the implementation of a psychiatry consult liaison (PCL) service. DESIGN: A retrospective observational study in the form of a chart review examining the utilization patterns of a psychiatric consultation liaison service in the inpatient rehabilitation setting. Chart review was performed to extract patient demographics (age and sex), rehabilitation diagnosis, cause of rehabilitation diagnosis (intentional, accident, self-inflicted, or disease), reason for referral to psychiatry, and psychiatric diagnosis on initial consultation. Statistical software was used for statistical analysis to answer the pre-specified research questions. SETTING: A 178 bed, free-standing, academic rehabilitation hospital located in an urban Canadian center. PATIENTS: Any patient admitted to the inpatient rehabilitation hospital who received a psychiatric consultation between September 2016 and December 2019 was eligible for inclusion. RESULTS: A total of 1016 charts were reviewed in the initial chart review and 1008 were included. The most common rehabilitation diagnoses that were associated with a psychiatric consult were (% admissions receiving consultation): amputations (38%); burns (35%), neurologic disorder (28%), deconditioning (14%), and musculoskeletal injury (7%). Although 20% of patients did not meet criteria for a psychiatric diagnosis, most common psychiatric diagnoses included mood disorder, adjustment disorder, neurocognitive disorder, and delirium. CONCLUSION: There are significant perceived needs for psychiatric services in the inpatient rehabilitation setting. Although some patient groups such as patients with amputations, burns, and trauma may exhibit the highest utilization, the service supports mental health needs from many patient groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".