Creation of a Laboratory for Statistics and Analysis of Dependence and Chronic Conditions: Protocol for the Bages Territorial Specialization and Competitiveness Project (PECT BAGESS)
Bibliographic record
Abstract
BACKGROUND: With the increasing prevalence of chronic diseases, partly due to the increase in life expectancy and the aging of the population, the complexity of the approach faced by the structures, dynamics, and actors that are part of the current care and attention systems is evident. The territory of Bages (Catalonia, Spain) presents characteristics of a highly complex ecosystem where there is a need to develop new, more dynamic structures for the various actors in the health and social systems, aimed at incorporating new actors in the technological and business field that would allow innovation in the management of this context. Within the framework of the Bages Territorial Specialization and Competitiveness Project (PECT BAGESS), the aim is to address these challenges through various entities that will develop 7 interrelated operations. Of these, the operation of the IDIAP Jordi Gol-Catalan Health Institute focuses on the creation of a Laboratory for Statistics and Analysis of Dependence and Chronic Conditions in the Bages region, in the form of a database that will collect the most relevant information from the different environments that affect the management of chronic conditions and dependence: health, social, economic, and environment. OBJECTIVE: This study aims to create a laboratory for statistical, dependence, and chronic condition analysis in the Bages region, to determine the chronic conditions and conditions that generate dependence in the Bages area, in order to propose products and services that respond to the needs of people in these situations. METHODS: PECT BAGESS originated from the Shared Agenda initiative, which was established in the Bages region with the goal of enhancing the quality of life and fostering social inclusion for individuals with chronic diseases. This study presents part of this broader project, consisting of the creation of a database. Data from chronic conditions and dependence service providers will be combined, using a unique identifier for the different sources of information. A thorough legal analysis was conducted to establish a secure data sharing mechanism among the entities participating in the project. RESULTS: The laboratory will be a key piece in the structure generated in the environment of the PECT BAGESS, which will allow relevant information to be passed on from the different sectors involved to respond to the needs of people with chronic conditions and dependence, as well as to generate opportunities for products and services. CONCLUSIONS: The emerging organizational dynamics and structures are expected to demonstrate a health and social management model that may have a remarkable impact on these sectors. Products and services developed may be very useful for generating synergies and facilitating the living conditions of people who can benefit from all these services. However, secure data sharing circuits must be considered. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/46542.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.082 | 0.021 |
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".