Oxford nanopore sequencing in clinical microbiology and infection diagnostics
Bibliographic record
Abstract
Extended turnaround times and large economic costs hinder the usage of currently applied screening methods for bacterial pathogen identification (ID) and antimicrobial susceptibility testing. This review provides an overview of current detection methods and their usage in a clinical setting. Issues of timeliness and cost could soon be circumvented, however, with the emergence of detection methods involving single molecule sequencing technology. In the context of bringing diagnostics closer to the point of care, we examine the current state of Oxford Nanopore Technologies (ONT) products and their interaction with third-party software/databases to assess their capabilities for ID and antimicrobial resistance (AMR) prediction. We outline and discuss a potential diagnostic workflow, enumerating (1) rapid sample prep kits, (2) ONT hardware/software and (3) third-party software and databases to improve the cost, accuracy and turnaround times for ID and AMR. Multiple studies across a range of infection types support that the speed and accuracy of ONT sequencing is now such that established ID and AMR prediction tools can be used on its outputs, and so it can be harnessed for near real time, close to the point-of-care diagnostics in common clinical circumstances.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".