Impact of Using Physiotherapy Self-Referral in the Medical–Surgical Neurological Intensive Care Unit
Bibliographic record
Abstract
PURPOSE: To describe physiotherapy (PT) referral practice in a medical-surgical neurological intensive care unit (MSNICU) of a large quaternary teaching hospital before and after the implementation of PT self-referral. METHODS: Charts were reviewed for MSNICU patients who received PT pre-implementation (Pre; n=90) and post-implementation (Post; n=100) to collect data on timeliness, number of referrals, and MSNICU length of stay (LOS); t-tests were conducted to determine group differences. RESULTS: The mean age of MSNICU patients referred to PT was 60.6 (SD=18.6) years; 59.5% were male. PT treatment consisted of cardiorespiratory (39% Pre, 51.1% Post), mobility (22% Pre, 28.8% Post), and combined (39% Pre, 20% Post) interventions. Overall, the number of days between MSNICU admission and PT initiation and MSNICU LOS did not differ significantly from Pre to Post. However, for patients (n=50) receiving early (within 7 days of MSNICU admission) PT self-referral Post versus patients receiving physician referral only Pre (n=83), there was a significant decrease (p=0.01) in time to PT initiation of 1.4 days (3.2 Pre, 1.8 Post). CONCLUSIONS: PT self-referral increased both the number of patients receiving more timely access to PT and the provision of treatment of a deferred group of patients previously not referred. Future studies need to evaluate the impact of referral methods across a variety of clinical populations.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".