Number needed to treat: A primer for neurointerventionalists
Bibliographic record
Abstract
BACKGROUND: The number needed to treat is a commonly used statistical term in modern neurointerventional practice. It represents the number of patients that need to be treated for one patient to benefit from an intervention. Given its growing popularity in reflecting study results, understanding the basics behind this statistic is of practical value to the neurointerventionalist. METHODS: Here, we review the basic theory and calculation of the number needed to treat, its application to stroke interventions, and its limitations. In addition, we demonstrate several simple methods of calculating the number needed to treat utilizing recent thrombectomy trial results. By presenting the number needed to treat as a universal metric, we provide a comprehensive comparative of the number needed to treat for key stroke therapies, including mechanical thrombectomy, tissue plasminogen activator, carotid endarterectomy, and prevention with antiplatelet and statin drugs. CONCLUSIONS: In comparison with available stroke therapies, mechanical thrombectomy stands out as the most effective acute intervention in patients with emergent large-vessel occlusions. Understanding how the number needed to treat is derived and its implications helps provide perspective to clinical trial data, identify health-care resource priorities, and improve communication with patients, health-care providers, and additional key stakeholders.
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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 | 0.139 | 0.304 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.014 | 0.045 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".