Shiga toxin-producing Escherichia coli and current trends in diagnostics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Shiga toxin-producing Escherichia coli (STEC) are bacterial pathogens responsible for deadly foodborne outbreaks and sporadic illnesses globally. Children under five are most susceptible to severe complications and death. Seven main serogroups (O157 and top six non-O157: O26, O45, O103, O111, O121, O145) have been identified as causing the majority of STEC infections in humans. Beef products are one frequent source of infection, necessitating robust surveillance programs. However, detection and isolation methods for clinically relevant serogroups have several inherent limitations, making routine screening for these pathogens difficult and time consuming. These pathogens are constantly evolving, further allowing them to evade current detection methods. Developments in technology and genomic sequencing may improve our knowledge of these pathogens, thereby enhancing surveillance systems. With intensive beef production systems and a growing global demand for food, such advances are essential to improve food safety.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it