Abstract 17: Time-Benefit Association is Magnified in Door-To-Puncture Window: Lose 1 Second, Lose 2.2 Hours of Healthy Life
Bibliographic record
Abstract
Background: The benefits of endovascular thrombectomy (EVT) are time dependent. Prior studies may have underestimated the magnitude of the time-benefit relation because time of onset (last known well ”LKW”) is imprecisely known, and analyses including late-arriving patients have under-representation of “fast-progressors.” Methods: Patient level data were pooled by the HERMES Investigators from all 7 RCTs of stent retriever thrombectomy devices (entirely or predominantly) versus medical therapy. Analysis was confined to early-treated patients (LKW-to-puncture≤4h). Exposures: last known well-to-door (LKWTD) time; door-to-puncture (DTP) time; door-to-reperfusion (DTR) time. Outcomes: stroke-related quality of life at 3m (utility-weighted modified Rankin Scale); years of healthy life lost [disability-adjusted life years (DALYs)]. Results: Among the 781 EVT-treated patients, 406 (52.0%) were treated within 4h of LKW, with LKW-to-Door time median 188 minutes (IQR 151-215) and DTP time 105 minutes (IQR 76-135). Among the 295/372 (79.3%) with substantial reperfusion, DTR time was median 145 minutes (IQR 111-186). Care process delays were more strongly associated with worse clinical outcomes in the DTP and DTR epochs than the LKW-To-Door epoch (Table 1A), e.g., for each 10 minute delay, healthy life-years lost were: DTP 1.8 months vs LKW-to-Door 0.0 months, p < 0.0001. Considering granular time increments, the amount of healthy life-years lost associated with each 1 second of delay was: DTP 2.2 hours, DTR 2.1 hours.(Table 1B) Conclusion: Post-arrival care delays are strongly associated with worse EVT patient outcomes in the early post-arrival time period. With every 1 second of delay in EVT delivery, patients lose 2.2 hours of healthy life-years. Continuous quality improvement to minimize delays in DTP and DTR for endovascular thrombectomy is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.002 |
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".