A biopsy of Breast Cancer mobile applications: state of the practice review
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most common cancer in women. The use of mobile software applications for health and wellbeing promotion has grown exponentially in recent years. We systematically reviewed the breast cancer apps available in today's leading smartphone application stores and characterized them based on their features, evidence base and target audiences. METHODS: A cross-sectional study was performed to characterize breast cancer apps from the two major smartphone app stores (iOS and Android). Apps that matched the keywords "breast cancer" were identified and data was extracted using a structured form. Reviewers independently evaluated the eligibility and independently classified the apps. RESULTS: A total of 1473 apps were a match. After removing duplicates and applying the selection criteria only 599 apps remained. Inter-rater reliability was determined using Fleiss-Cohen's Kappa. The majority of apps were free 471 (78.63%). The most common type of application was Disease and Treatment information apps (29.22%), Disease Management (19.03%) and Awareness Raising apps (15.03%). Close to 1 out of 10 apps dealt with alternative or homeopathic medicine. The majority of the apps were intended for patients (75.79%). Only one quarter of all apps (24.54%) had a disclaimer about usage and less than one fifth (19.70%) mentioned references or source material. Gamification specialists determined that 19.36% contained gamification elements. CONCLUSIONS: This study analyzed a large number of breast cancer-focused apps available to consumers. There has been a steady increase of breast cancer apps over the years. The breast cancer app ecosystem largely consists of start-ups and entrepreneurs. Evidence base seems to be lacking in these apps and it would seem essential that expert medical personnel be involved in the creation of medical apps.
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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 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".