Q&A: Antibiotic resistance: what more do we know and what more can we do?
Bibliographic record
Abstract
Is the problem of antibiotic resistance getting worse? Yes. Resistance to antibiotics continues to be a significant and growing medical problem across the globe. In the US, the Centers for Disease Control recently released a report showing that infections due to carbapenemresistant Enterobacteriaceae (CRE), which are associated with mortality rates between 40% and 50%, rose from 1.2% to 4.2% over the decade from 2001 to 2011 [1]. In the clinically important Klebsiella subset of these pathogens, the rise over the same time period was from 1.6% to 10.4%. Carbapenems are among the last resort antibiotics we have to treat infections of Gramnegative bacteria and this steady erosion of their efficacy is especially concerning. The cause is the spread of genes that encode enzymes that destroy these antibiotics, in particular KPC (Klebsiella pneumoniae carbapenemase) and NDM (New Delhi metallo-betalactamase) [2]. The latter has been found widespread in the environment, including the water supply on the Indian subcontinent [3]. Infections due to multidrug resistant Neisseria gonorrhea are also on the rise. Once easily treated with available antibiotics, the emergence of drug-resistant strains resulting in clinical failures is becoming more common. A recent study in a Toronto clinic showed that 6.77% of cases could not be cured with standard oral antibiotic therapy [4]. Outbreaks of infections caused by multidrug and sometimes pan-resistant epidemic clones of Acinetobacter baumannii are increasingly reported in health care settings across the globe. The establishment of methicillin-resistant Staphylococcus aureus (MRSA) in the community at large as well as in hospitals is continuing; over 460,000 MRSA infections required hospitalization in the US in 2009 [5]. There are now circulating
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.095 | 0.052 |
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".