Breaking down the Desirability of Outcome Ranking (DOOR): unpacking the methodology behind the Dalbavancin as an Option for Treatment of Staphylococcus aureus Bacteraemia (DOTS) trial
Bibliographic record
Abstract
Desirability of Outcome Ranking (DOOR) outcomes such as the primary outcome of the DOTS (Dalbavancin as an Option for Treatment of Staphylococcus aureus Bacteremia) trial can improve statistical efficiency (requiring smaller sample sizes) compared to binary outcomes, and potentially improve clinical decision making by integrating treatment effects on all relevant clinical outcomes. However, standardised outcome components may not accurately capture the true hypothesised treatment effect, and bespoke DOOR outcomes may not be generalisable. Investigators should carefully consider different DOOR components when selecting trial outcomes and when performing sample size calculations.
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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 | 0.723 | 0.863 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".