Cohort Profile: Swiss Personalized Health Network Cohort Consortium
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Swiss cohort studies provide high-quality longitudinal data, but finding and comparing relevant studies across cohorts has historically been challenging. The Swiss Personalized Health Network Cohort Consortium (SPHN-CC) was established to address these limitations by creating the first coordinated network of Swiss cohort studies within the internationally recognized Maelstrom Research catalogue. Methods Participating cohorts were invited in 2021–2022, including longitudinal and cross-sectional studies with 1010-21 993 participants. Data collected include questionnaires, physical and cognitive assessments, administrative records, and biological samples. Variables were classified into 18 domains and 134 subdomains, and an online metadata catalogue was implemented to document study designs, explore variable content, and assess harmonization potential. Results The catalogue enables researchers to identify study-specific and harmonized variables for co-analysis. Core variables, such as age, sex/gender, anthropometrics, and medication use, are widely available, while other variables vary across cohorts. Harmonization assessments demonstrate that several key variables can be co-analyzed across multiple studies, supporting collaborative research with over 37’000 participants. A use case illustrates the potential for harmonizing and co-analyzing data across studies. Conclusions The SPHN-CC strengthens Swiss cohort research by enhancing data discoverability, supporting harmonization, and facilitating cross-cohort and international research, providing a model for more efficient use of high-value longitudinal data. Key features The Swiss Personalized Health Network Cohort Consortium aims to optimize the use of data and biological samples collected by publicly funded Swiss cohort studies. Up to now, 10 studies participated in the initiative. From 1988 to 2020 they together recruited over 50 000 participants. Recruitment remains active for six of the studies. Most cohorts are still collecting data and biological samples. All studies collected information from questionnaires, nine also collected biospecimens, seven performed physical measurements, two conducted cognitive assessments and two retrieved information from administrative databases at least once during the life course of the study. An online study and variables catalogue was developed to help researchers determine whether data collected might serve to answer the specific research questions they would like to address and, if relevant, may be harmonized and co-analyzed across studies. Access to the metadata catalogue is open and free.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it