Experiences of Patient-Led Surveillance, Including Patient-Performed Teledermoscopy, in the MEL-SELF Pilot Randomized Controlled Trial: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Current clinician-led melanoma surveillance models require frequent routinely scheduled clinic visits, with associated travel, cost, and time burden for patients. Patient-led surveillance is a new model of follow-up care that could reduce health care use such as clinic visits and medical procedures and their associated costs, increase access to care, and promote early diagnosis of a subsequent new melanoma after treatment of a primary melanoma. Understanding patient experiences may allow improvements in implementation. OBJECTIVE: This study aims to explore patients' experiences and perceptions of patient-led surveillance during the 6 months of participation in the MEL-SELF pilot randomized controlled trial. Patient-led surveillance comprised regular skin self-examination, use of a mobile dermatoscope to image lesions of concern, and a smartphone app to track and send images to a teledermatologist for review, in addition to usual care. METHODS: Semistructured interviews were conducted with patients previously treated for melanoma localized to the skin in New South Wales, Australia, who were randomized to the patient-led surveillance (intervention group) in the trial. Thematic analysis was used to analyze the data with reference to the technology acceptance model. RESULTS: We interviewed 20 patients (n=8, 40% women and n=12, 60% men; median age 62 years). Patients who were more adherent experienced benefits such as increased awareness of their skin and improved skin self-examination practice, early detection of melanomas, and opportunities to be proactive in managing their clinical follow-up. Most participants experienced difficulty in obtaining clear images and technical problems with the app. These barriers were overcome or persevered by participants with previous experience with digital technology and with effective help from a skin check partner (such as a spouse, sibling, or friend). Having too many or too few moles decreased perceived usefulness. CONCLUSIONS: Patients with melanoma are receptive to and experience benefits from patient-led surveillance using teledermoscopy. Increased provision of training and technical support to patients and their skin check partners may help to realize the full potential benefits of this new model of melanoma surveillance.
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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 | 0.060 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".