Development of a new thrombectomy technical difficulty index: TTDI
Bibliographic record
Abstract
Aim: Multiple recent trials have proven the efficacy of thrombectomy in large vessel occlusive stroke and earlier reperfusion correlates with improved outcomes. We developed a thrombectomy technical difficulty index (TTDI) to predict the expected procedural difficulty as an aid to operator decision making for the achievement of a fast and successful recanalization.Materials and Methods: Key thrombectomy factors were used to grade predicted difficulty of thrombectomy on a 3-point scale, from minimal, mild to moderate to severe. Thirty patients that underwent thrombectomy had their computed tomography angiograms scans analysed by seven neurointerventionists using the TTDI to predict level of difficulty to establish its reliability (intra-class correlation, ICC) and validity.Results: An almost perfect level of agreement on TTDI scores between the 7 neurointerventionists was reported (ICC = 0.89, 95 CI = 0.81 to 0.94), and an expert INR opinion of case difficulty using the TTDI (ICC = 0.861, 95 CI = 0.77 to 0.93). Validity analysis showed that that length of procedure was shorter for minimal compared to mild to moderate difficultly cases as assessed with TTDI.Conclusion: The TTDI is a promising tool to assess predicted thrombectomy case difficulty, allowing operator to consider potential problems and inform decisions about whether a modification to technique, including access, equipment and anaesthesia, should be considered. Larger prospective studies evaluating the TTDI are warranted.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".