Abstract WMP95: Drip and Ship vs. Mothership: Conditional Probability Modelling for Patients With Suspected Large Vessel Occlusion
Bibliographic record
Abstract
Introduction: For patients with suspected large vessel occlusion (LVO) stroke should we bypass alteplase treatment at Primary Stroke Centres (PSC) in favor of endovascular therapy (EVT) at Comprehensive Stroke Centres (CSC) (mothership) or transport the patient to the PSC for alteplase and then transfer to the CSC for EVT (drip and ship)? This is complicated by the inability to definitively diagnose LVO stroke without imaging. Methods: The efficacy decay of alteplase and EVT over time and the accuracy of the Los Angeles Motor Scale (LAMS) LVO screening tool were combined with various treatment times to predict the probability of good outcome (mRS 0 - 1 at 90 days) for both transport strategies for patients with LAMS ≥4. Results: The results are shown in the Figure. If the patient is closest to the CSC mothership is always superior. If treatment is fast at both centres drip and ship is superior if the centres are far apart and the patient must travel past a PSC to get to a CSC, otherwise the strategies are equivalent (Panel A). Slow treatment at the PSC increases the area where mothership is superior and eliminates drip and ship unless the time from onset to alteplase administration exceeds 4.5 hours in the mothership scenario (Panel B). Slow treatment at both centres decreases the mothership area and expands the drip and ship area (Panel C). Conclusions: Due to the uncertainty in patient diagnosis both transport options are equivalent in most scenarios. A triaging tool with greater positive predictive value would increase the size of the mothership areas. However, even with diagnosis uncertainty the importance of fast treatment times is illustrated.
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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".