Clinical practice guideline recommendation summaries for pediatric oncology health care professionals: A qualitative study
Bibliographic record
Abstract
OBJECTIVE: To develop a summary format of clinical practice guideline (CPG) recommendations to improve understandability among health care professionals. METHODS: We developed a summary format based on current research and used the "Think Aloud" technique in one-on-one cognitive interviews to iteratively improve it. Interviews of health care professionals from Children's Oncology Group-member, National Cancer Institute Community Oncology Research Program sites were conducted. After every five interviews (a round), responses were reviewed, and changes made to the format until it was well understood and no new, substantive suggestions for revision were raised. We took a directed (deductive) approach to content analysis of the interview notes to identify concerns related to recommendation summary usability, understandability, validity, applicability and visual appeal. RESULTS: During seven rounds of interviews with 33 health care professionals, we identified important factors that influenced understandability. Participants found understanding weak recommendations more challenging than strong recommendations. Understanding was improved when the term 'conditional' recommendation was used instead of 'weak' recommendation. Participants found a Rationale section to be very helpful but desired more information when a recommendation entailed a practice change. In the final format, the recommendation strength is clearly indicated in the title, highlighted, and defined within a text box. The rationale for the recommendation is in a column on the left, with supporting evidence on the right. In a bulleted list, the Rationale section describes the benefits and harms and additional factors, such as implementation, that were considered by the CPG developers. Each bullet under the supporting evidence section indicates the level of evidence with an explanation and the supporting studies with hyperlinks when applicable. CONCLUSIONS: A summary format to present strong and conditional recommendations was created through an iterative interview process. The format is straightforward, making it easy for organizations and CPG developers to use it to communicate recommendations clearly to intended users.
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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 | 0.048 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".