Improving coronary artery bypass grafting: a systematic review and meta-analysis on the impact of adopting transit-time flow measurement
Bibliographic record
Abstract
Despite there being numerous studies of intraoperative graft flow assessment by transit-time flow measurement (TTFM) on outcomes after coronary artery bypass grafting (CABG), the adoption of contemporary TTFM is low. Therefore, on 31 January 2018, a systematic literature search was performed to identify articles that reported (i) the amount of grafts classified as abnormal or which were revised or (ii) an association between TTFM and outcomes during follow-up. Random-effects models were used to create pooled estimates with 95% confidence intervals (CI) of (i) the rate of graft revision per patient, (ii) the rate of graft revision per graft and (iii) the rate of graft revision among grafts deemed abnormal based on TTFM parameters. The search yielded 242 articles, and 66 original articles were included in the systematic review. Of those articles, 35 studies reported on abnormal grafts or graft revisions (8943 patients, 15 673 grafts) and were included in the meta-analysis. In 4.3% of patients (95% CI 3.3-5.7%, I2 = 73.9) a revision was required and 2.0% of grafts (95% CI 1.5-2.5%; I2 = 66.0) were revised. The pooled rate of graft revisions among abnormal grafts was 25.1% (95% CI 15.5-37.9%; I2 = 80.2). Studies reported sensitivity ranging from 0.250 to 0.457 and the specificity from 0.939 to 0.984. Reported negative predictive values ranged from 0.719 to 0.980 and reported positive predictive values ranged from 0.100 to 0.840. This systematic review and meta-analysis showed that TTFM could improve CABG procedures. However, due to heterogeneous data, drawing uniform conclusions appeared challenging. Future studies should focus on determining the optimal use of TTFM and assessing its diagnostic accuracy.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".