Delphi consensus recommendations on how to provide cardiovascular rehabilitation in the COVID-19 era
Bibliographic record
Abstract
This Delphi consensus by 28 experts from the European Association of Preventive Cardiology (EAPC) provides initial recommendations on how cardiovascular rehabilitation (CR) facilities should modulate their activities in view of the ongoing coronavirus disease 2019 (COVID-19) pandemic. A total number of 150 statements were selected and graded by Likert scale [from -5 (strongly disagree) to +5 (strongly agree)], starting from six open-ended questions on (i) referral criteria, (ii) optimal timing and setting, (iii) core components, (iv) structure-based metrics, (v) process-based metrics, and (vi) quality indicators. Consensus was reached on 58 (39%) statements, 48 'for' and 10 'against' respectively, mainly in the field of referral, core components, and structure of CR activities, in a comprehensive way suitable for managing cardiac COVID-19 patients. Panelists oriented consensus towards maintaining usual activities on traditional patient groups referred to CR, without significant downgrading of intervention in case of COVID-19 as a comorbidity. Moreover, it has been suggested to consider COVID-19 patients as a referral group to CR per se when the viral disease is complicated by acute cardiovascular (CV) events; in these patients, the potential development of COVID-related CV sequelae, as well as of pulmonary arterial hypertension, needs to be focused. This framework might be used to orient organization and operational of CR programmes during the COVID-19 crisis.
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 | 0.220 | 0.174 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".