Abstract 195: Drip and Ship vs. Mothership: a Comparison of Two Different Conditional Probability Models
Bibliographic record
Abstract
Introduction: There is uncertainty about the best treatment method for patients with suspected large vessel occlusion (LVO) stroke. The drip and ship [alteplase at a Primary Stroke Centre (PSC) and then transfer to a Comprehensive Stroke Centre (CSC) for endovascular therapy (EVT)] and mothership (bypassing the PSC in pursuit of EVT at a CSC) transport methods have been proposed. Earlier, these two methods were evaluated for patients with assumed known LVO. As LVO cannot be diagnosed without imaging we present an update for patients with suspected LVO using the Los Angeles Motor Scale (LAMS) screening tool. Methods: The expected distribution of LVO, non-LVO occlusions (nLVO), intracranial hemorrhages (ICH), and stroke mimics (SM) for patients with LAMS≥4 was combined with time dependent probability of good outcome for alteplase (for LVO and nLVO) and EVT, and probability of good outcome for ICH and SM to create conditional probability models for drip and ship and mothership scenarios. Results were mapped and compared with the previous model. Results: For patients with LAMS≥4 drip and ship and mothership predict equivalent outcomes in most areas (Figure-Panel A). Drip and ship is only relevant at PSCs that are far from CSCs. Increasing door to needle time (DNT) decreases the size of drip and ship areas. This contrasts with the prior model (Figure-Panel B) where mothership is more dominant especially as DNT increases. Conclusions: The inclusion of nLVO, ICH, and SM patients introduces important differences in modelling patient transport. This diagnosis uncertainty decreases the relative difference between the probabilities of good outcome for the two transport options.
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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 | 0.048 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".