The FIM™ as a measure of change in function after discharge from inpatient rehabilitation: a Canadian perspective
Bibliographic record
Abstract
PURPOSE: To examine the FIM™ as an outcome measure at follow-up following discharge from inpatient rehabilitation. METHODS: Secondary analysis of the National Rehabilitation Reporting System (NRS) data from 13 facilities across Canada that collected follow-up data between 2001 and 2006. The study sample included all NRS records with a hospital length of stay of at least 3 days, for individuals 18 years and older. Outcomes included: mean total, motor and cognitive FIM™ scores at admission, discharge, and follow-up; change in FIM™ scores from admission to discharge and from discharge to follow-up; correlation between FIM™ scores at admission, discharge and follow-up, and predictors of the change in FIM™ scores between discharge and follow-up. RESULTS: The majority of the change in FIM™ scores is seen between admission and discharge with the higher FIM scores maintained, if not increased slightly, between discharge and follow-up. Discharge and follow-up total FIM™ scores are highly correlated indicating that collection of the follow-up FIM™ may not provide additional information that justifies the expense of data collection after a patient has been discharged from inpatient rehabilitation. CONCLUSIONS: The use of more appropriate rehabilitation follow-up outcomes needs to be considered.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".