Impact of a hidradenitis suppurativa patient decision aid on treatment decision making: A randomized controlled trial
Bibliographic record
Abstract
Background Patient decision aids are tools that facilitate shared decision making when clinical evidence and patient values and preferences inform the process. Evidence-based guidelines have been developed for clinicians in hidradenitis suppurativa management. To address treatment decision-making needs of hidradenitis suppurativa patients, we developed a hidradenitis suppurativa patient decision aid. Objective To assess the efficacy of the hidradenitis suppurativa patient decision aid during treatment decision making. Methods An online, participant-blinded, parallel-group, randomized controlled trial of the hidradenitis suppurativa patient decision aid versus Mayo Clinic hidradenitis suppurativa website content (Mayo) was conducted with hidradenitis suppurativa patients. Outcomes were knowledge, decisional conflict, and preparation for decision making. Results Forty participants fulfilled inclusion criteria and were randomized to hidradenitis suppurativa patient decision aid or Mayo. In the hidradenitis suppurativa patient decision aid group, data from 16 and 15 participants were analyzed at phases 1 and 2, respectively. In the Mayo group, data from 15 and 13 participants were analyzed at phases 1 and 2, respectively. Increased knowledge ( P < .01) and preparation for decision making ( P < .01), as well as reduced decisional conflict ( P < .01), were observed in the hidradenitis suppurativa patient decision aid compared with the Mayo group. Limitations The online methodology and recruitment from online hidradenitis suppurativa support groups limits generalizability of findings. Conclusion A hidradenitis suppurativa patient decision aid increased knowledge and preparation for decision making and reduced decisional conflict.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".