Futile and Harmful Reperfusion and the Balance Between Treatment Effect and Overall Outcomes in Stroke
Bibliographic record
Abstract
Over the past decades, ischemic stroke research has primarily focused on achieving rapid reperfusion. Endovascular thrombectomy has revolutionized the treatment paradigm for patients with large vessel occlusion, with recent trials showing benefit even in patients with large core at baseline. These findings have led some to advocate for reperfusion in all cases, regardless of infarct size and severity. We critically examine this line of reasoning and introduce 2 important caveats. First, in an individual patient, reperfusion does not necessarily or uniformly translate into meaningful improvement and favorable outcomes. The concept of futile reperfusion is real. As a corollary, trial results capture average effects, and individuals have a wider range of outcomes. Furthermore, results are often reported as relative rather than absolute treatment effects. As baseline prognosis worsens, the absolute likelihood of a good outcome may fall below a threshold where the intervention is no longer justified, despite a favorable relative treatment effect. Second, in a small subset of patients, reperfusion may actively worsen outcome; this is harmful reperfusion. While additional harm may seem negligible in such a high-risk population, this rationale is flawed as it encourages therapeutic actionism and violates the foundational medical ethical principle of primum non nocere. To advance patient care, we must move beyond a one-size-fits-all reperfusion model that focuses only on vessel reopening. Some patients might have infarcts that are simply too large (eg, >150 mL), ischemia that is too severe (eg, severe noncontrast computed tomography hypodensity), or comorbidities that overwhelm any potential benefit. A more nuanced approach requires a better understanding of tissue viability, perfusion physiology, and ischemic damage. This would allow for refined patient selection by leveraging advanced imaging and large-scale data sets to develop accurate models to predict treatment effect, that is, beneficial, futile, and harmful reperfusion.
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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 | 0.063 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".