Post-stroke rehabilitation
Bibliographic record
Abstract
The aim of this study was to examine predictors of discharge of hospitalized stroke patients to either an acute inpatient rehabilitation facility (IRF) or subacute skilled nursing facility (SNF).A retrospective cohort study was done in a large multicampus urban academic medical center of individuals hospitalized for stroke between January 1, 2015 and December 31, 2015 and who were discharged to either an IRF (n = 84) or SNF (n = 59). A set of characteristics and scales were collected on each patient and assessed using univariate and multivariate regression analyses.Although univariate analyses revealed multiple measures were associated with discharge destination, the most predictive multivariate logistic regression model for discharge to SNF incorporated age (odds ratio [OR] = 1.09, 95% confidence interval [CI], 1.05-1.13), premorbid physical disability (OR 7.52, 95% CI 1.66-34.14), and inability to ambulate before discharge (OR 5.84, 95% CI 2.01-16.92) with an overall c-statistic of 0.85.Increasing age, premorbid physical disability, and inability to ambulate increase the overall likelihood of discharge to a SNF. These findings need to be replicated in larger samples to determine whether they are generalizable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".