A Methodological Approach that Integrates Offline and Digital Environments in Scientific Medical Research on aging “fra-SET”
Bibliographic record
Abstract
This paper documents the experience of a methodological approach that integrates offline, personalized, and individual assessment with new digital data processing technologies, based on specific medical needs. In the multicenter non-pharmacological experimental study on aging titled "Fra-set: Identification and Quantification of Frailty" by P. Abete (funded by the National Recovery and Resilience Plan (PNRR) - AGE-it - Ageing Well in an Ageing Society, Spoke 3, Task 1.2 (Definition of a shared minimum dataset and data collection framework: Multidimensional assessment of age-related diseases, multimorbidity, and frailty and related outcomes in health settings), conducted within the study "Metabolic Aspects of Vascular Diseases: Importance in the Development of Atherosclerosis and Identification of New Therapeutic Approaches and Biomarkers" (PRIN 2020), the endpoint is the validation of the diagnostic tool named “fr-AGILE,” which allows for the identification and quantification of frailty in hospitalized elderly patients in facilities with varying levels of care intensity. Although the investigative tools are questionnaires, the research is quantitative. Data collection occurs in non-digital settings, specifically in low, medium, and high-intensity care facilities affiliated with the study in Campania. It utilizes information acquired directly from the patient or caregiver in a detailed and individualized manner, through the administration of scales such as the Edmonton Frail Scale and fr-AGILE, tests administered at clinical stability (pre-discharge). The importance of adherence to informed consent from patients—whose absence constitutes an exclusion criterion from the study; the awareness of a pronounced digital divide within the elderly population; the urgent need for extreme personalization of care; the necessity to identify a medical tool for the identification and quantification of frailty in elderly patients that is simple, quick, and multidimensional (Faller JW et al., 2019); the need to abandon Fried's phenotypic model (the narrow biological paradigm) in favor of a complex bio-psycho-social paradigm that includes, in a multidimensional approach, the estimation of physical status, the psycho-cognitive sphere, functional aspects, and social aspects (P. Abete et al., 2017)—are motivations that dictate the need for a research methodology that integrates offline, individualized, and personalized data collection tools with digital contexts for data analysis, processing, and sharing, as well as internal communication and interconnection between the UOCs and the various professional figures involved in the study. Keywords: Aging, Customization, Fragility, Integrated methodology
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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 | 0.360 | 0.376 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".