ERS CONNECT CRC: a peer-reviewed list of digital respiratory technologies
Bibliographic record
Abstract
Background: A collaboration between the ERS Clinical Research Collaboration (CRC) CONNECT and the European Union Digital Health Uptake (DHU) initiative has been established to create a searchable, open-access, user-controlled, European-wide repository of available digital respiratory health technologies. Methods: We adopted a co-design methodology to develop a standardised peer review process to assess applications for inclusion in the repository. Eligible owners of respiratory technologies apply through the DHU Radar [1] and are evaluated by an expert panel from the CONNECT network based on predefined criteria (clinical evidence, regulatory compliance, usability, implementation feasibility). Technologies that meet the required standards receive a 3-year "CONNECT Listed" distinction, with periodic re-evaluation for continued compliance. Results: A standardised peer review process was defined and beta-tested with the support of a group of 13 volunteers. The CONNECT repository (Figure 1), hosted by DHU Radar [2], is ready to start listing digital respiratory technologies. Conclusion: We now have a robust and efficient evaluation system for streamlined implementation of the CONNECT repository to promote efficient knowledge exchange and discovery of available digital respiratory health solutions across the EU. [1] https://digitalhealthuptake.eu/radar-repository/ [2] https://digitalhealthuptake.eu/ers-connect-repository/ erj;66/suppl_69/PA2040/F1 F1 F1
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 | 0.035 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.430 | 0.358 |
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".