Abstract WMP85: Recovery Rate vs. Recovery Capacity: A Mathematical Model and its Implications for Rehabilitation
Bibliographic record
Abstract
INTRODUCTION: Recovery of most hemiparetic patients at 90 days can be well predicted as a fixed proportion (70%) of initial motor deficit. However, recent work has shown considerable variability in the rate of recovery among proportional recoverers, prompting consideration of whether rate of recovery and recovery capacity are independent and whether a single rate dynamic governs proportional recovery. HYPOTHESIS: Among proportional recoverers, recovery rate variability can be accounted for by a single mathematical model in which: 1) recovery rate is independent of recovery capacity and 2) recovery has a sigmoid trajectory parameterized only by initial stroke severity. METHODS: We studied 23 patients with first-ever unilateral hemiparetic stroke previously identified as proportional recoverers. Fugl-Meyer Upper Extremity Motor Exam (FM-UE) had been measured at <72h, 1 week, and 90 days. A non-linear model predicting patients’ FM-UE score at any time after stroke onset was posited and model parameters were estimated by regressing one-week FM-UE scores against initial scores. Statistical significance and goodness of fit were evaluated. RESULTS: The model accounted for 86% of variability in motor recovery achieved by patients at 1 week after stroke onset (pseudo-R 2 =0.863, F 23,21= 418.0, p <.0001) and predicted that more severely impaired patients will have a slower maximum recovery rate and a recovery period that is longer in duration and more delayed in onset. CONCLUSION: The model provides evidence that proportional recovery is governed by a single rate dynamic and that recovery rate is independent of recovery capacity. It provides a tool for predicting motor impairment at any time following stroke onset and suggests a framework for characterizing the biology of recovery and the role of therapeutic interventions as either capacity-enhancing or rate-enhancing.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".