Reflections from Infection Prevention 2015: beating the bugs, improving the systems and thinking outside the box
Bibliographic record
Abstract
Beating the bugsThe conference kicked off with Professor Andreas Voss.By Andreas' own admission, he was given a curve-ball of a title: 'CRE, VRE, C. difficile or MRSA: what should be the priority of infection prevention?' Andreas developed a framework for grading the priority of our microbial threats, accounting for transmissibility, virulence, antibiotic resistance, at-risk patients, feasibility of decolonisation, cost and impact of uncontrolled spread, and the result?Any and all microbes that cause healthcare-associated infection (HCAI) should be a priority of infection prevention.Even those that seem to have less clinical impact (such as VRE) are good indicators of system failure.If we focus too much on one threat, we risk losing sight of the bigger picture.Next, Dr Jennifer Gardy (Canada) told the story of an outbreak using whole genome sequencing (WGS).I was struck by how far DNA sequencing has come in such a short time frame: to think that you can now sequence a genome in a USB drive on your laptop!The roadblock to its useful application is now not the sequencing per se, but the bioinformatics and useful clinical application.But, if that can be solved, WGS offers the potential to turn a 'plate of spaghetti' epidemiological picture into a clear transmission network, and allow you to 'read outbreaks like a book'.Jennifer used the now infamous outbreak of carbapenemresistant enterobacteriaceae at the National Institutes of Health Clinical Center as an example, which, in reality was really rather small; perhaps that is the point (Palmore and Henderson, 2013).However, the potential applications of WGS go far beyond simple outbreak investigation, encompassing epidemic tracking and metagenomics, and even find a routine application in clinical microbiology (Roach et al., 2015).It was significant that only 8% of the audience had participated in a WGS study; I suspect this will be 98% in 5 years' time.Carole Fry's guest lecture offered some historical perspective, tracking the introduction of first standard and then transmission based precautions from the early 1990s.Although standard precautions are a great idea in principle, they (ironically) lack standardisation!The Centers for Disease Prevention & Control (CDC) family of precautions Reflections from Infection Prevention 2015: beating the bugs, improving the systems and thinking outside the box
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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 | 0.034 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.027 | 0.072 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".