Mobile Screening Units for the Early Detection of Breast Cancer and Cardiovascular Disease: A Pilot Telemedicine Study in Southern Italy
Bibliographic record
Abstract
Introduction: Telemedicine is the use of Information and Communication Technologies (ICT) to improve patient outcomes by increasing access to care, medical information and services. The aim of this pilot study was to evaluate and support the implementation of screening and early detection programs in the prevention of breast cancer and cardiovascular diseases with the establishment of a remote diagnosis through the use of ICT in mobile units. Materials and Methods: A total of 430 individuals were recruited in an area of Southern Italy. Particularly, 321 women were recruited to undergo breast cancer screening in accordance with Italian guidelines. Likewise, cardiovascular screening interested 109 subjects. A self-contained mobile unit with connectivity was provided to offer breast and cardiovascular screenings. To maximize the benefit, we have evaluated the return of investment. Results: The telemedicine screening program allowed the detection of early pathologies. In breast cancer screening, 40.8% of cases were negative to lesions, 34.9% were positive to benign lesions, and 3.1% presented suspicious malignant lesions; these lesions were further checked by histological analyses, which showed a positive response in 70% of cases. The cardiovascular screening concerned 109 participants based on age and other risk factors. We observed a significant difference among risk factors in patients with cardiac disease (p < 0.001); particularly, hypertension was significantly the most present risk factor (51.4%, p < 0.05), followed by smoking (28.4%, p < 0.05). A cardiovascular pathology was detected in 40.4% of enrolled subjects. A 3.3:1 return on investment was calculated. Conclusion: Our findings demonstrate that telemedicine may represent a promising approach to deliver several health services, such as screening programs, with users who cannot utilize services in their locations. The use of telemedicine on diagnostic campers greatly reduces the costs of screening for breast cancer and major cardiovascular diseases within the Southern Italian Health Service. We believe that public investment can have a further significant return on investment by implementing the principles of precision medicine.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".