Systematic review of the quality of clinical guidelines for aphasia in stroke management
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Aphasia affects up to 38% of stroke survivors. Clinical guidelines can improve patient care and outcomes. Given the importance of aphasia management in stroke care, the purpose of this study was to systematically search for, retrieve and assess the quality of currently published clinical guidelines for aphasia in stroke management. METHOD: Systematic search of bibliographic resources, publications, association websites, databases, Internet and pearling revealed multidisciplinary stroke and speech pathology-specific clinical guidelines, which were evaluated using the Appraisal of Guidelines and Research and Evaluation (AGREE) II tool. Guidelines obtaining a rigour of development score above 66.67% in AGREE II evaluations underwent further ADAPTE Collaboration tool analysis. RESULTS: There was significant variability in methodological rigour, reporting of guideline development processes and scope of coverage of recommendations pertaining to aphasia management provided within the guidelines. The Australian Clinical Guidelines for Stroke Management (2010) and New Zealand Clinical Guidelines for Stroke Management (2010) achieved the highest scores (74% and 81%, respectively) in AGREE II analysis and both obtained a 'yes' in all seven ADAPTE domains. The Scottish Intercollegiate Guideline Network 108 (2008) guideline achieved 73% in AGREE II and six out of seven 'yes' in ADAPTE, however, contained no aphasia-specific recommendations. The Royal College of Speech and Language Therapists (2005) guideline provided the most comprehensive aphasia coverage, however, demonstrated lower methodological rigour in AGREE II (64%) and ADAPTE evaluations (three 'yes' out of seven). CONCLUSION: Improvement is needed in the quality of methodological rigour in development and reporting within clinical guidelines, and in aphasia-specific recommendations within stroke multidisciplinary clinical guidelines.
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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 | 0.062 | 0.312 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".