About antifragility and the challenge of dealing with endovascular therapy trials that fail to show a positive result
Bibliographic record
Abstract
Endovascular therapy (EVT) is a highly effective treatment for acute ischemic stroke due to large vessel occlusion (LVO). Eight randomized controlled trials have proven its efficacy and safety in patients with LVO presenting within 24 hours.1–3 EVT eligibility criteria are likely to expand even further into the ‘fringes’, as more and more data on the safety and efficacy of EVT beyond current guideline recommendations become available—for example, for patients with M2 occlusions4 and those presenting more than 24 hours from last known well.5 In fact, it is becoming increasingly difficult to find a patient subgroup which does not benefit from EVT. While in the early days of EVT we were asking ourselves which patients to treat, the situation has now turned around; today, we ask ourselves which patients should not be treated.6 Physicians pursue a more and more aggressive treatment strategy: patient age, time from symptom onset, comorbidities, distal occlusion site, low Alberta Stroke Program Early CT Score (ASPECTS), and National Institutes of Health Stroke Scale (NIHSS) score do not discourage us from offering EVT .7 As an example, current guideline recommendations from the American Heart Association/American Stroke Association restrict level 1A treatment recommendations for EVT to patients with internal carotid artery and M1 occlusions, ASPECTS ≥6, and NIHSS score ≥6.8 However, in a recent international multidisciplinary survey with 607 physicians from 38 countries, most stated that they would offer EVT even in patients with further distal occlusions, and ASPECTS and NIHSS scores <6 (table 1).7 View this table: Table 1 Results from UNMASK EVT, an international multidisciplinary case based survey The scenario illustrated in figure 1 is not covered by currently established guidelines. Nevertheless, we and many others think that treating this patient was the right decision. The case clearly shows a dilemma that we encounter in our clinical practice …
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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 | 0.526 | 0.792 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.028 | 0.033 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.032 | 0.031 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".