Enhanced external counterpulsation for management of symptoms associated with long COVID
Bibliographic record
Abstract
Study objective: Enhanced external counterpulsation (EECP) as a possible therapy for Long COVID. Design: Retrospective analysis of a contemporary, consecutive patient cohort. Setting: 7 outpatient treatment centers. Participants: Long COVID patients. Intervention: 15-35 EECP treatments. Main outcome measures: The change from baseline in 1) Patient Reported Outcome Measurement Information System (PROMIS) Fatigue; 2) Seattle Angina Questionnaire (SAQ); 3) Duke Activity Status Index (DASI); 4) 6-Minute Walk Test (6MWT); 5) Canadian Cardiovascular Society (CCS) Angina Grade; 6) Rose Dyspnea Scale (RDS); and 7) Patient Health Questionnaire (PHQ-9). Results: Compared to baseline, the PROMIS Fatigue, SAQ, DASI, and 6MWT improved by 4.63 ± 3.42 (p < 0.001), 21.44 ± 16.54 (p < 0.001), 18.08 ± 13.82 (p < 0.001), and 200.00 ± 180.14 (p = 0.002), respectively. CCS and RDS improved in 63% and 44% of patients, respectively. All patients unable to work prior to EECP were able to return post-therapy. Conclusions and relevance: EECP significantly improved validated fatigue and cardiovascular-related markers in patients with Long COVID.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".