Bringing Proportional Recovery into Proportion: Bayesian Hierarchical Modelling of Post-Stroke Motor Performance
Bibliographic record
Abstract
Abstract Accurate predictions of motor performance after stroke are of cardinal importance for the patient, clinician, and health care system. More than ten years ago, the proportional recovery rule was introduced by promising just that: high-fidelity predictions of recovery following stroke based only on the initially lost motor performance, at least for a specific fraction of patients. However, emerging evidence suggests that this recovery rule is subject to various confounds and may apply less universally than assumed by many. We systematically revisited stroke outcome predictions by casting the data in a less confounded form and employing more integrative and flexible hierarchical Bayesian models. We jointly analyzed n =385 post-stroke trajectories from six separate studies – the currently largest overall dataset of upper limb motor recovery. We addressed confounding ceiling effects by introducing a subset approach and ensured correct model estimation through synthetic data simulations. Finally, we used model comparisons to assess the underlying nature of recovery within our empirical recovery data. The first model comparison, relying on the conventional fraction of patients called fitters , pointed to a combination of constant and proportional to lost function recovery. Proportional to lost here describes the original notion of proportionality, indicating greater recovery in case of a more pronounced initial deficit. This combination explained only 32% of the variance in recovery, which is in stark contrast to previous reports of >80%. When instead analyzing the complete spectrum of subjects, model comparison selected a composite of constant and proportional to spared function recovery, implying a more significant improvement in case of more preserved function. Explained variance was at 53%. Therefore, our data suggest that motor recovery post-stroke may exhibit some characteristics of proportionality. However, the levels of explanatory value were substantially reduced compared to what has previously been reported. This finding motivates future research moving beyond solely behavior scores to explain stroke recovery and establish robust single-subject predictions.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".