Number needed to treat (NNT) to compare benefits of letrozole (LET) with adjuvant chemotherapy (CT) in patients (pts) with node-positive (N+) breast cancer (BC)
Bibliographic record
Abstract
581 Background: Adjuvant CT is an accepted treatment for N+ BC. The addition of taxanes to anthracycline based adjuvant CT has been demonstrated to improve overall survival (OS) in all but one study as in table below. BIG 1–98 has reported a survival gain with the aromatase inhibitor LET compared to tamoxifen (TAM). NNT (the reciprocal of absolute benefit) is the number of pts that need to be treated with a new intervention to avoid 1 additional event over the benefit provided by standard therapy. For multiple numerical outcomes from randomized trials, NNT is an effective method to express results in a clinically meaningful way. To evaluate the degree of benefit of LET over TAM in women with early stage N+ BC in BIG 1–98, we compared the NNT for survival benefit from LET to that needed to see benefit from the addition of a taxane to anthracycline based CT based on clinical trial data in BC pts with N+ disease. Methods: 5-yr survival data were taken from the pivotal randomized controlled trial (RCT) for LET (N+ pts from BIG 1–98, TAM arm censored for crossover to LET) and from 4 RCTs of adjuvant CT trials, AC-T, FEC-D, and DAC. NNT was calculated with respect to OS at 5 yrs; outcome is presented as the NNT to save a life (see Table). Results: The NNT to save a life for adjuvant LET vs TAM is comparable to the NNT for the addition of a taxane to anthracycline based CT in women with N+ BC. Conclusions: The magnitude of survival benefit of LET over TAM in terms of NNT is comparable to that needed to see a survival benefit from paradigm- changing modern adjuvant CT regimens. F= fluorouracil, E = epirubicin, C = cyclophosphamide, D = docetaxel, T = paclitaxel, A = doxorubicin [Table: see text] [Table: see text]
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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 | 0.055 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".