Role of Oral Intake, Mobility, and Activity Measures in Informing Discharge Recommendations for Hospitalized Inmate and Noninmate Patients With COVID-19: Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND: Patients who were incarcerated were disproportionately affected by COVID-19 compared with the general public. Furthermore, the impact of multidisciplinary rehabilitation assessments and interventions on the outcomes of patients admitted to the hospital with COVID-19 is limited. OBJECTIVE: We aimed to compare the functional outcomes of oral intake, mobility, and activity between inmates and noninmates diagnosed with COVID-19 and examine the relationships among these functional measures and discharge destination. METHODS: A retrospective analysis was performed on patients admitted to the hospital for COVID-19 at a large academic medical center. Scores on functional measures including the Functional Oral Intake Scale and Activity Measure for Postacute Care (AM-PAC) were collected and compared between inmates and noninmates. Binary logistic regression models were used to evaluate the odds of whether patients were discharged to the same place they were admitted from and whether patients were being discharged with a total oral diet with no restrictions. Independent variables were considered significant if the 95% CIs of the odds ratios (ORs) did not include 1.0. RESULTS: A total of 83 patients (inmates: n=38; noninmates: n=45) were included in the final analysis. There were no differences between inmates and noninmates in the initial (P=.39) and final Functional Oral Intake Scale scores (P=.35) or in the initial (P=.06 and P=.46), final (P=.43 and P=.79), or change scores (P=.97 and P=.45) on the AM-PAC mobility and activity subscales, respectively. When examining separate regression models using AM-PAC mobility or AM-PAC activity scores as independent variables, greater age upon admission decreased the odds (OR 0.922, 95% CI 0.875-0.972 and OR 0.918, 95% CI 0.871-0.968) of patients being discharged with a total oral diet with no restrictions. The following factors increased the odds of patients being discharged to the same place they were admitted from: being an inmate (OR 5.285, 95% CI 1.334-20.931 and OR 6.083, 95% CI 1.548-23.912), "Other" race (OR 7.596, 95% CI 1.203-47.968 and OR 8.515, 95% CI 1.311-55.291), and female sex (OR 4.671, 95% CI 1.086-20.092 and OR 4.977, 95% CI 1.146-21.615). CONCLUSIONS: The results of this study provide an opportunity to learn how functional measures may be used to better understand discharge outcomes in both inmate and noninmate patients admitted to the hospital with COVID-19 during the initial period of the pandemic.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".