The Use and Impact of a Decision Support Tool for Appendicitis Treatment
Bibliographic record
Abstract
OBJECTIVE: Since introducing new and alternative treatment options may increase decisional conflict, we aimed to describe the use of the decision support tool (DST) and its impact on treatment preference and decisional conflict. BACKGROUND: For the treatment of appendicitis, antibiotics are an effective alternative to appendectomy, with both approaches associated with a different set of risks (eg, recurrence vs surgical complications) and benefits (eg, more rapid return to work vs decreased chance of readmission). Patients often have limited knowledge of these treatment options, and DSTs that include video-based educational materials and questions to elicit patient preferences about outcomes may be helpful. Concurrent with the Comparing Outcomes of Drugs and Appendectomy trials, our group developed a DST for appendicitis treatment ( www.appyornot.org ). METHODS: A retrospective cohort including people who self-reported current appendicitis and used the AppyOrNot DST between 2021 and 2023. Treatment preferences before and after the use of the DST, demographic information, and Ottawa Decisional Conflict Scale (DCS) were reported after completing the DST. RESULTS: A total of 8243 people from 66 countries and all 50 U.S. states accessed the DST. Before the DST, 14% had a strong preference for antibiotics and 31% for appendectomy, with 55% undecided. After using the DST, the proportion in the undecided category decreased to 49% ( P < 0.0001). Of those who completed the Ottawa Decisional Conflict Score (DCS; n = 356), 52% reported the lowest level of decisional conflict (<25) after using the DST; 43% had a DCS score of 25 to 50, 5.1% had a DCS score of >50 and 2.5% had and DCS score of >75. CONCLUSIONS: The publicly available DST appyornot.org reduced the proportion that was undecided about which treatment they favored and had a modest influence on those with strong treatment preferences. Decisional conflict was not common after use. The use of this DST is now a component of a nationwide implementation program aimed at improving the way surgeons share information about appendicitis treatment options. If its use can be successfully implemented, this may be a model for improving communication about treatment for patients experiencing emergency health conditions.
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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 | 0.013 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".