Some Key Issues Relating to the Reporting and Interpretation of Time-to-Event Data
Bibliographic record
Abstract
To the Editor: Griffiths et al recently reported that in a cohort of Australian patients with ankylosing spondylitis included in the Optimising Patient outcomes in Australian RheumatoLogy (OPAL) dataset, the median persistence (persistence defined as the time to discontinuation of treatment) was longest for patients treated with golimumab (GOL) in all lines of therapy, and shortest for those treated with etanercept (ETN).1 In drawing this conclusion, the authors have overlooked some statistical aspects relating to the reporting of time-to-event data that make it difficult to evaluate the robustness of their conclusions. Griffiths et al1 stated that log-rank tests were used to investigate differences between the Kaplan-Meier (KM) estimates. … Address correspondence to Dr. I.M. Schou, NHMRC Clinical Trials Centre, University of Sydney, 92-94 Parramatta Road, Sydney, NSW 2050, Australia. Email: manjula.schou{at}sydney.edu.au.
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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 | 0.074 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.028 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 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".