Abstract TMP83: Drip and Ship vs. Mothership: The Effect of Large Vessel Occlusion Screening Tools on Conditional Probability Modelling
Bibliographic record
Abstract
Introduction: Ischemic stroke with large vessel occlusion (LVO) cannot be definitively diagnosed without imaging. Clinical screening tools to help identify LVO in the field have variable positive predictive value (PPV). The accuracy and PPV of a clinical screen tool for LVO impacts transport decisions. Methods: Using conditional probability modelling, we estimated the effect of screening tool diagnostic properties for LVO. The PPV of various screening tools (generated from a prospective study of suspected stroke patients), including two hypothetical scales with PPVs of 60% and 75% respectively, were combined with the efficacy decay of alteplase and EVT over time to predict the probability of good outcome (mRS 0 - 1 at 90 days) for 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 strategies. Results: The results are shown in the Figure. As the PPV of the tool increases, the areas where mothership predicts the best outcome increases. At longer treatment times at the PSC the drip and ship area also decreases as PPV increases. The absolute probability of good outcome also decreases as PPV increases because more LVO strokes, with inherently poorer outcomes than most false positives, are identified. Conclusions: The PPV of the screening tool greatly impacts transport decision making. Due to the efficacy of efficacy of EVT for LVO stroke as PPV increases so does the area where mothership predicts the greatest probability of good outcome.
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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 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".