Long-Term Effectiveness of a Decision Support App (Pink Journey) for Women Considering Breast Reconstruction Surgery: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Various kinds of breast reconstruction (BR) options, including implants and autologous, and surgery techniques, including traditional and endoscope assisted, can be used to perform surgery. All options have their own advantages and disadvantages. Women decide on an option depending on the values and preferences they emphasize. Lacking knowledge about BR or having decision difficulties during the treatment decision process makes women experience more decision regret, psychological distress, and poor body image. Delivering decision support with a values clarification exercise using eHealth approaches would be beneficial for patient outcomes. OBJECTIVE: This study aims to examine the effects of a decision support app on decision-making quality and psychological morbidity for women considering BR surgery. METHODS: This randomized controlled trial included women who were over 20 years of age and were newly diagnosed with breast cancer and candidates for mastectomy. Women having an option for breast conservation were excluded. After being referred from the outpatient physician, the women provided consent and completed the baseline assessment. Women allocated to the control group (CG) received usual care and were provided with a pamphlet with information about types of surgery and the advantages and disadvantages of different surgery types. Women allocated to the intervention group (IG) were given the same pamphlet and guided to use the Pink Journey app to support their decision. Then they were also prompted to discuss the opinions with their significant others. Finally, the decision-making process of using the app was printed out for women that they could take home. Decision conflict, anxiety, and depression were measured at baseline. At 1 week after the intervention (T1) and at 1 month (T2), 8 months (T3), and 12 months (T4) after surgery, the women completed decision conflict, decision regret, anxiety, depression, and body image scales. An intention-to-treat analysis was performed. RESULTS: From February 2018 to July 2019, 96 women were randomly assigned to the CG (n=48) or the IG (n=48). Results revealed that body image distress declined significantly for the IG but increased for the CG. The interaction of time and group also reached significance, indicating a significant decrease in body image distress from baseline in the IG compared with the CG after the 12th month (T4) follow-up (β=-2.25, standard error=1.01, P=.027). However, there was no significant difference in decision conflict (P=.21-.87), decision regret (P=.44-.55), anxiety (P=.26-.33), and depression (P=.20-.75), indicating that the decrease in these outcomes in the IG was not greater than those in the CG. CONCLUSIONS: Although we found no effect on decision conflict, decision regret, anxiety, and depression, a decision aid that combines surgery information and values clarification can help women reduce their body image distress. TRIAL REGISTRATION: ClinicalTrials.gov NCT04190992; https://clinicaltrials.gov/ct2/show/NCT04190992.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".