Bibliographic record
Abstract
How often have you treated an individual with stroke who, based on your experience and expertise, should have regained function but did not. What is going on? Routinely we generate a list of reasons to explain why sometimes our clients fail to recover function when seemingly they should thrive: poor motivation, concomitant medical problems, lack of family support, inadequate rehabilitation interventions, and so on. What if we had better tools with which to categorize some of these patients? Maybe we do. In their article, Pohl et al describe a cognitive deficit associated with disrupted executive function after stroke. Switching between tasks is an essential component of normal function and an ability that most of us take absolutely for granted. In their research, Pohl et al used an elegant method demonstrating that task switching is not a motor function but rather a cognitive function. More importantly for physical therapists, the ability to switch between 2 tasks was impaired by the most common distribution of stroke: the middle cerebral artery. This switching deficit, or “switch cost,” was particularly acute when the switch was not externally cued (ie, it was under “endogenous” control); yet, endogenous control of switching between tasks is essential for independent function. What makes this work even more relevant to physical therapists is the finding that a switching deficit was noted in individuals in the subacute phase (1–3 months) after stroke; a period that corresponds to the typical time frame for rehabilitation.
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.
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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.151 | 0.079 |
| Insufficient payload (model declined to judge) | 0.033 | 0.020 |
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".