Remote ischaemic conditioning before exercise: are we there yet?
Bibliographic record
Abstract
In recent years we have witnessed substantial progress in the treatment of patients with ischaemic heart disease. Interventional techniques are improving and medical therapy is more effective. In particular, there has been dramatic progress in the treatment of patients with ST elevation myocardial infarction. These patients are being treated effectively by primary percutaneous coronary interventions, restoring flow to the ischaemic heart tissue, together with intensive medical treatment. However, in spite of this progress, ischaemia-reperfusion injury and its consequences remain a significant issue. Agents that were thought to be cardioprotective, including antioxidants and anti-inflammatory agents as well as adenosine, have not proved to be effective.1 There are some encouraging results from small studies,2 but so far none of the large trials has shown a beneficial effect of any medication in reducing ischaemia-reperfusion injury. In contrast, the results of studies involving ischaemic conditioning have shown a more powerful effect than individual medications that target only one pathway. Ischaemic conditioning is an innate protective phenomenon by which brief episodes of ischaemia protect the organs from prolonged and potentially lethal ischaemia. Ischaemic conditioning was found to be effective in different organs, but its potential to protect the heart is probably …
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.021 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".