The Impact of Marketing Language on Patient Preference for Robot-Assisted Surgery
Bibliographic record
Abstract
Robot-assisted surgery is gaining momentum as a new trend in minimally invasive surgery. With limited evidence supporting its use in place of the far less expensive conventional laparoscopic surgery, it has been suggested that marketing pressure is partly responsible for its widespread adoption. The impact of phrases that promote the novelty of robot-assisted surgery on patient decision making has not been investigated. We conducted a discrete choice experiment to elicit preference of partial colectomy technique for a hypothetical diagnosis of colon cancer. A convenience sample of 38 participants in an ambulatory general surgery clinic consented to participate. Each participant made 2 treatment decisions between robot-assisted surgery and conventional laparoscopic surgery, with robot-assisted surgery described as "innovative" and "state-of-the-art" in one of the decisions (marketing frame), and by a disclosure of the uncertainty of available evidence in the other (evidence-based frame). The magnitude of the framing effect was large with 12 of 38 subjects (31.6%, P = .005) selecting robot-assisted surgery in the marketing frame and not the evidence-based frame. This is the first study to our knowledge to demonstrate that words that highlight novelty have an important influence on patient preference for robot-assisted surgery and that use of more neutral language can mitigate this effect.
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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 | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".