Systematic review of effectiveness and quality assessment of patient education materials and decision aids for breathlessness
Bibliographic record
Abstract
BACKGROUND: Around 10% of adults suffer from clinically significant breathlessness. High quality and actionable patient education materials (PEMs) and patient decision aids (PDAs) have an important role for shared decision making and patient self-management. OBJECTIVE: To systematically assess the effectiveness of patient education materials (PEMs) and patient decision aids (PDAs) on clinical outcomes. Secondly, to assess the quality of PEMs and PDAs for breathlessness that are accessible online. METHODS: A systematic review of PEM or PDA intervention for breathlessness published between 1 January 2010 and November 2020 was conducted. An environmental scan and quality assessment of publicly available PEMs and PDAs was also conducted. RESULTS: Out of 2985 records, five studies were eligible for inclusion in this systematic review. Results of two randomised controlled trials suggest potential effectiveness of PEMs to improve patient reported outcomes and reduce healthcare utilization. In the environmental scan, 88 materials were included. Minimum reading age for most was high (Grade 10) and PEMs scored an average of 87% for understandability and 67% for actionability. Based on the DISCERN tool only 10 were classified as high quality. CONCLUSION: There is a paucity of evidence on the effectiveness of PEMs and PDAs for improvement in breathlessness. There is a need to develop higher quality PEMs for breathlessness.
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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 | 0.020 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".