The authors reply:
Bibliographic record
Abstract
Dr. Schuetz argues that the value of procalcitonin (PCT) for predicting bacteremia should be judged on what it adds to the clinical assessment, rather than area-under-the curve metrics alone, and that PCT results should be interpreted in the context of a patient's specific clinical presentation.Indeed, this is true of all laboratory and radiologic investigations.Dr. Schuetz cites an observational study suggesting that combining PCT with clinical scoring systems may increase positive predictive value for positive blood cultures and could in theory reduce blood culture tests with relatively few missed positive culture results [1].Notwithstanding the challenge of getting clinicians to use and rely upon complex scoring systems, the impact of missed positive blood cultures needs to be considered.A positive blood culture may be the only means through which a bacterial pathogen and its antibiotic susceptibility can be identified.Without these data, patients are at increased risk of misdiagnosis and inappropriate antibiotic therapy, increasing their risk for poor outcomes [2,3].Are the potential savings on blood culture sampling that could be made through incorporation of
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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.047 | 0.042 |
| Insufficient payload (model declined to judge) | 0.012 | 0.014 |
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".