Clinical practice recommendations for management of lateropulsion after stroke determined by a Delphi expert panel
Bibliographic record
Abstract
OBJECTIVE: People exhibiting post-stroke lateropulsion actively push their body across the midline to the more affected side and/or resist weight shift toward the less affected side. Despite its prevalence and associated negative rehabilitation outcomes, no clinical practice guidelines exist for the rehabilitation of post-stroke lateropulsion. We aimed to develop consensus-based clinical practice recommendations for managing post-stroke lateropulsion using an international expert panel. DESIGN: This Delphi panel process conformed with Guidance on Conducting and Reporting Delphi Studies recommendations. PARTICIPANTS: Panel members had demonstrated clinical and/or scientific background in the rehabilitation of people with post-stroke lateropulsion. MAIN MEASURES: The process consisted of four electronic survey rounds. Round One consisted of 13 open questions. Subsequent rounds ascertained levels of agreement with statements derived from Round One. Consensus was defined a priori as ≥75% agreement (agree or strongly agree), or ≥70% agreement after excluding 'unsure' responses. RESULTS: Twenty participants completed all four rounds. Consensus was achieved regarding a total of 119 recommendations for rehabilitation approaches and considerations for rehabilitation delivery, positioning, managing fear of falling and fatigue, optimal therapy dose, and discharge planning. Statements for which 'some agreement' (50%-74% agreement) was achieved and those for which recommendations remain to be clarified were recorded. CONCLUSIONS: These recommendations build on existing evidence to guide the selection of interventions for post-stroke lateropulsion. Future research is required to elaborate specific rehabilitation strategies, consider the impact of additional cognitive and perceptual impairments, describe positioning options, and detail optimal therapy dose for people with lateropulsion.
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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 | 0.293 | 0.282 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".