Bibliographic record
Abstract
Sir—I thank Dr. Lapin for pointing out the error regarding the reference by Merle-Melet et al. [1] in my recent article “Foodborne Listeriosis” [2]. Indeed, the study by Merle-Melet et al. suggests that a combination of amoxicillin and trimethoprim-sulfamethoxazole (TMP-SMZ), not rifampin, may be preferable to ampicillin and aminoglycoside for the treatment of listeriosis. The intracellular location of Listeria monocytogenes suggests that an antibiotic that has good intracellular activity, such as TMP-SMZ or rifampin, might eradicate this population of sequestered organisms. When used as a single agent, trimethoprim appears to have more activity than does sulfamethoxazole, but the combination of the 2 agents is highly active in vivo. Rifampin is also highly active in vitro and, although not synergistic, may be additive in its effect; therefore, a combination of rifampin and TMP-SMZ would be worth consideration for the treatment of a patient who is allergic to penicillin. Although in vitro antagonism between ampicillin and rifampin has been demonstrated [3, 4], the combination is active in in vitro models of infection. An excellent review of antibiotic treatment of listeriosis recently has been published by Hof et al. [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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.036 | 0.034 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".