The number remaining at risk: an adjunct to the number needed to treat.
Bibliographic record
Abstract
Although the number of patients needed to treat (NNT) to prevent an adverse clinical event is of great clinical value to practising physicians, it is limited in that it fails to provide a measure of prognosis among patients not achieving benefit. For example, if the NNT is 100, what is likely to happen to the other 99? The number remaining at risk (NRR), which is an index that enhances the value of the NNT, is described. The NRR is the ratio of the residual event rate among treated patients and the absolute reduction in outcome events (NRR = experimental event rate [EER]/control event rate [CER]-EER), where EER and CER are the event rates among experimental and control groups, respectively. This index represents the number of events likely to occur among the NNT, or the odds of experiencing an adverse outcome event as opposed to deriving benefit from therapy. The NRR is a simple index that can easily be calculated from the published results of a clinical trial. As an adjunct to the NNT, it provides a measure of the impact of therapy and the average prognosis of remaining patients.
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 | 0.112 | 0.370 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".