Medication errors in critical care: risk factors, prevention and disclosure
Bibliographic record
Abstract
Introduction We sought to evaluate the impact of pretreatment with intravenous thrombolysis (IVT) on the rate and speed of successful reperfusion (SR) in patients with emergent large vessel occlusion (ELVO) treated with mechanical thrombectomy (MT) in a high-volume tertiary care stroke center. Methods Consecutive patients with ELVO treated with MT were evaluated. Outcomes were compared between patients who underwent combined IVT and MT (IVT+MT) and those treated with direct MT (dMT). The elapsed time between groin puncture to beginning of reperfusion (GPTBRT) and the numbers of device passes required to achieve SR were also documented. Results A total of 287 and 132 patients were treated with IVT+MT and dMT, respectively. The IVT+MT group had higher SR (73.8% vs 62.9%; p=0.023) and 3-month functional independence (modified Rankin Scale score 0–2;51.6% vs 38.2%; p=0.008) rates. The median GPTBRT was shorter in the IVT+MT group (48 (IQR 33–70) vs 70 (IQR 44–98) min; p<0.001). Among patients who achieved SR (n=292), the median number of required device passes was lower in the IVT+MT subgroup (1 (IQR 1–1) vs 2 (IQR 1–2); p<0.001), while the rate of patients requiring ≤2 device passes was higher (98% vs 77%; p<0.001). IVT+MT was independently related to higher odds of SR (OR 1.64; 95% CI 1.03 to 2.61; p=0.036) and shorter GPTBRT (unstandardized linear regression coefficient −20.39; 95% CI −27.56 to –13.22; p<0.001) on multivariable analyses adjusting for potential confounders. Among patients with SR, IVT+MT was independently associated with a higher likelihood of ≤2 device passes (OR 14.63; 95% CI 4.46 to 48.00; p<0.001). Conclusions IVT pretreatment appears to increase the rates of SR and shortens the duration of the endovascular procedure by requiring fewer device passes in patients with ELVO treated with MT.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".