EMERGENCE AND MECHANISMS OF MULTI-DRUG RESISTANT MICROORGANISMS IN PATIENTS AT HIGH RISK FOR ANTIMICROBIAL RESISTANCE
Bibliographic record
Abstract
Antimicrobial resistance (AMR) poses a substantial threat to public health and clinical medicine. By 2050, it’s predicted that AMR will be responsible for a yearly mortality rate of 10 million people, surpassing the mortality of cancer. Despite this daunting future we face, there are many efforts currently employed to combat the growth of AMR. One significant effort involves surveillance and early identification of novel resistant bacteria circulating in high antibiotic exposure environments. The second chapter of this thesis focuses on sampling 25 patients from a hospital environment, rich with antibiotics, to build a collection of AMR bacteria that will be tested and added to surveillance efforts/future study. This chapter allowed for the identification of several worrying AMR bacteria that provide greater insights into circulating AMR in Canadian hospitals and their patients. From the AMR collection created in chapter 2, we are also able to advance our scientific understanding of how antibiotic resistance develops within us and causes issues with treatment. In chapter 3, we looked at the effects of antibiotic administration routes on the level of AMR observed in our patient sample. We saw that current approaches to limit selection for AMR in the gut still resulted in clinically significant and concerning increases in AMR. Furthermore, this chapter allowed greater understanding of contributors to increased AMR in patients. AMR increases are not fully explained by exposure/colonization in hospital settings, but also by evolution of AMR originating from non-resistant bacteria in the gut. Additionally, analysis of these bacteria will inform expected AMR evolutionary trajectories and help us plan against them. During analysis of patient data, we also came across evolution of a less understood resistance phenotype, hetero-resistance, to a very important antibiotic, colistin. We investigated a commonly prescribed antifungal, fluconazole, for its ability to promote this resistance phenotype; however, it appeared that fluconazole did not promote this phenotype. Ultimately, this thesis serves as a valuable reservoir of AMR bacteria for future study and contributes to a greater understanding of AMR development in patients, one day leading to more informed clinical decision making.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".