Do’s and DOTS: contextualizing a clinical trial evaluating dalbavancin use in Staphylococcus aureus bacteraemia
Bibliographic record
Abstract
As practicing infectious disease physicians, clinical trialists, and regular prescribers of dalbavancin, we read the recently published dalbavancin as an option for treatment of Staphylococcus aureus bacteraemia (DOTS) trial by Turner et al. [1] with great interest. We can expect to see an increased use of long-acting lipoglycopeptides in the future, based on the number of real-world reports that are being published [2–4], the fact that the patent for dalbavancin is expiring in the European Union in February 2026 (Anna Flodgren, Medical Advisor Nordics at ADVANZ PHARMA, 1 October 2025, personal communication), and that the expected compound annual growth rate is 12.5% for dalbavancin from 2026 to 2033 [5].
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 | 0.146 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".