Should we use the transradial approach in cardiogenic shock?
Bibliographic record
Abstract
Transradial approach in cardiogenic shock 409 flow after PCI was more frequently observed in the TRA group; however, angiographic core laboratory validation was not performed.Overall, reported bleeding rates were very low.Although recent studies of TRA as compared with TFA did not demonstrate a significant difference in treatment delays, there were previous concerns that TRA might prolong door -to -balloon times.10,11 Reassuringly, in this study, there was no difference in delays between symptoms onset and first medical contact or coronary angiography between the groups.This analysis of TFA as compared with TRA in CS is nonrandomized and so is only hypothesis--generating.It is likely that residual confounding does exist despite propensity matching given the absolute difference in mortality.Patients and centers that used TRA in CS are likely different than those that used TFA.However, randomized trials of TRA as compared with TFA access in CS are unlikely to be performed given the body of evidence supporting TRA outside of CS.Another important finding from this study is that high -volume operators have better outcomes in patients with acute myocardial infarction and CS particularly with TRA than lower volume operators.In conclusion, these data support safety and feasibility of TRA in patients in CS, who often do not have a palpable radial artery.Given the wealth of data, a radial -first approach should be considered in CS.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".