Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: Prescribing the most appropriate dose of motor therapy for individual patients is a challenge because minimal data are available and a large number of factors are unknown. This review explores the concept of dose and reviews the most recent findings in the field of neurorehabilitation, with a focus on relearning motor skills after stroke. RECENT FINDINGS: Appropriate dosing involves the prescription of a specific amount of an active ingredient, at a specific frequency and duration. Dosing parameters, particularly amount, are not well defined or quantified in most studies. Compiling data across studies indicates a positive, moderate dose-response relationship, indicating that more movement practice results in better outcomes. This relationship is confounded by time after stroke, however, wherein longer durations of scheduled therapy may not be beneficial in the first few hours, days, and/or weeks. SUMMARY: These findings suggest that substantially more movement practice may be necessary to achieve better outcomes for people living with the disabling consequences of stroke. Preclinical investigations are needed to elucidate many of the unknowns and allow for a more biologically driven rehabilitation prescription process. Likewise, clinical investigations are needed to determine the dose-response relationships and examine the potential dose-timing interaction in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it