Utility of the number needed to treat in paediatric haematological cancer randomised controlled treatment trials: a systematic review
Bibliographic record
Abstract
OBJECTIVES: The primary objective was to assess the utility of the number needed to treat (NNT) to inform decision-making in the context of paediatric oncology and to calculate the NNT in all superiority, parallel, paediatric haematological cancer, randomised controlled trials (RCTs), with a comparison to the threshold NNT as a measure of clinical significance. DESIGN: Systematic review DATA SOURCES: MEDLINE, EMBASE and the Cochrane Childhood Cancer Group Specialized Register through CENTRAL from inception to August 2018. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Superiority, parallel RCTs of haematological malignancy treatments in paediatric patients that assessed an outcome related to survival, relapse or remission; reported a sample size calculation with a delta value to allow for calculation of the threshold NNT, and that included parameters required to calculate the NNT and associated CI. RESULTS: A total of 43 RCTs were included, representing 45 randomised questions, of which none reported the NNT. Among acute lymphoblastic leukaemia (ALL) RCTs, 29.2% (7/24) of randomised questions were found to have a NNT corresponding to benefit, in comparison to acute myeloid leukaemia (ALM) RCTs with 50% (3/6), and none in lymphoma RCTs (0/13). Only 28.6% (2/7) and 33.3% (1/3) had a NNT that was less than the threshold NNT for ALL and AML, respectively. Of these, 100% (2/2 ALL and 1/1 AML) were determined to be possibly clinically significant. CONCLUSIONS: We recommend that decision-makers in paediatric oncology use the NNT and associated confidence limits as a supportive tool to evaluate evidence from RCTs while placing careful attention to the inherent limitations of this measure.
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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 | 0.147 | 0.449 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.026 | 0.022 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".