Intramyocardial Injection of Autologous Platelet-Rich Plasma Combined with Transmyocardial Revascularization
Bibliographic record
Abstract
Transmyocardial revascularization (TMR) can improve refractory angina but does not consistently demonstrate an effect on myocardial function. Recent studies suggest a synergistic effect between TMR and exogenously supplied growth factors. We evaluated the clinical role of intramyocardial injection of autologous platelet-rich plasma (PRP) in conjunction with TMR. Twenty-five nonrevascularizable patients with class III/IV angina underwent minimally invasive sole therapy TMR during a 5-year period at a single institution. Group 1 (14 patients) underwent TMR alone while group 2 (11 patients) underwent TMR plus injection of PRP (Magellan plasma separator) between TMR channels. Blinded angina assessment and ejection fraction (EF) were measured preoperatively and at 6 months postoperatively. Baseline EF (57 +/- 10% vs. 50 +/- 7%), angina class (3.7 +/- 0.5 vs. 3.7 +/- 0.5), and the number of channels (48 +/- 5 vs. 48 +/- 4) were statistically similar in both groups. At 6 months, two class angina relief was similar in both groups (92% vs. 100%, p = 0.4); however, the TMR + PRP group had a lower average angina score (1.3 vs. 0.4, p = 0.07) and more were angina free (23% vs. 78%, p = 0.04) than the TMR-alone group. EF improved in the TMR + PRP group (-2.0% vs. +9.0%, p = 0.07) compared to the TMR-alone group. Two 30-day morbidities occurred in the TMR-alone group (atrial fibrillation and left pleural effusion) and one mortality occurred in the TMR + PRP group. Intramyocardial injection of autologous PRP combined with TMR may be more efficacious at relieving angina and improving myocardial function than TMR alone.
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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 | 0.000 | 0.000 |
| 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".