A Mobile App for Thyroid Cancer Patients Aiming to Enhance Their Quality of Life: Protocol for a Quasiexperimental Interventional Pilot Study
Bibliographic record
Abstract
BACKGROUND: Thyroid cancer (TC) is one of the fastest growing cancers all over the world. Differentiated thyroid cancer (DTC) is the most frequent subtype of TC. When appropriate treatment is given, the prognosis for the patient is generally excellent. Despite the generally good prognosis of thyroid carcinomas, the symptoms may range from emotional to physical discomfort, depending on the thyroid hormone status, which can severely affect the patient. Moreover, the diagnostic and therapeutic procedures that DTC patients have to undergo, such as thyroidectomy and radioiodine therapy, significantly affect their mental and physical well-being. Often, the physician only addresses the favorable prognosis of DTC compared with other cancer types and neglects to assess issues related to the quality of life (QoL) of the patient; this was the reason we decided to design a mobile app for DTC patients and their caregivers. OBJECTIVE: The aim of this study is to research the feasibility and applicability of an mHealth app tailored to DTC patients, as reflected in their QoL. The main features of the developed app offer access to useful information about thyroid cancer, diagnostic tests, and the appropriate therapy administered to DTC patients. METHODS: Based on the existing literature, we created an up-to-date information platform regarding TC and especially DTC. In order to develop an effective app that can be implemented in current health care, we designed a section where the patient and physician can keep a medical record in an effort to enable access to such information at any time. Finally, we designed a user-friendly notification program, including pill prescription, follow-up tests, and doctor visit reminders in order to equally facilitate the lives of the patient and physician. RESULTS: Having developed this mobile app, we aim to conduct a pilot quasiexperimental interventional trial. Our intention is to enroll at least 30 TC patients and assign them to intervention or control groups. Both groups will receive standard care for treating and monitoring TC, and the intervention group will also receive and use the DTC app. TC patients' QoL will be assessed for both control and intervention groups in order to examine the effectiveness of the DTC app. QoL will be assessed through the QoL core questionnaire European Organisation for Research and Treatment of Cancer (EORTC) QLQ-THY34 in combination with the EORTC QLQ-C30 questionnaire through quantitative statistical analysis. CONCLUSIONS: The use of mHealth apps can play a significant role in patient education, disease self-management, remote monitoring of patients, and QoL improvement. However, the main limitation of the majority of existing studies has been the lack of assessing their usefulness as well as the absence of specific instruments to carry out this assessment. In light of those considerations, we developed a mobile app tailored to the needs of DTC patients. Furthermore, we evaluated its contribution to the QoL of the patients by using the EORTC QLQ-THY34 questionnaire, an accurate and safe instrument for the evaluation of the QoL in TC patients, while supporting future planned endeavors in the field. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/13409.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".