The Orpington Prognostic Scale for patients with stroke: Reliability and pilot predictive data for discharge destination and therapeutic services
Bibliographic record
Abstract
PURPOSE: To determine the inter-rater and test-retest reliability of the Orpington Prognostic Scale (OPS) in patients with stroke. Pilot data were gathered to evaluate its predictive validity for discharge destination and therapeutic services required on discharge. METHOD: Ninety-four consecutive patients, admitted to hospital due to stroke participated. Pairs of physiotherapists (PT) and occupational therapists (OT) assessed patients using the OPS on days 7 and 14 post stroke. For inter-rater reliability, one rater performed the OPS while the other observed, each scoring the scale independently. For test-retest reliability, two different raters tested the subjects separately within the same day. Data were gathered on the discharge destination and the number of follow-up services prescribed. RESULTS: The inter-rater reliability as measured by the intraclass correlation coefficient (ICC) was 0.99 (95% CI 0.97 - 0.99). For test-retest reliability, the ICC was 0.95 (95% CI 0.90 - 0.98). The accuracy for predicting discharge to home using OPS 5.0 was 65% (95% CI 0.52 - 0.76). OPS scores were not related to number of follow-up services prescribed. CONCLUSIONS: Despite high inter-rater and test-retest reliability, the OPS has limited predictive accuracy for discharge destination and is a poor predictor of follow-up services.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".