Explaining unexplained diarrhea and associating risks and infections
Bibliographic record
Abstract
Gastrointestinal illnesses are common afflictions. However, knowledge of their etiology is often lacking. Moreover, most cases of infections with reportable enteric pathogens (Campylobacter jejuni, Escherichia coli O157:H7, Salmonella, Shigella, Yersinia, Cryptosporidia and Giardia) have sporadic modes of acquisition, yet control measures are often biased towards mitigation of risks discerned by outbreak analysis. To determine the etiology of unexplained diarrhea it is important to study populations that can be matched to appropriate controls and to couple thorough classic microbiologic evaluation on receipt of specimens with archiving and outgrowth capabilities. Research evaluations should address the potential roles of a broad panel of candidate bacterial pathogens including diarrheagenic E. coli, Listeria monocytogenes, Helicobacters and jejuni Campylobacters, and also apply novel massively parallel sequencing and nucleic acid detection technologies that allow the detection of viral pathogens. To fill voids in our knowledge regarding sources of known enteric pathogens it will be critical to extend case-control studies to assess risk factors and exposures to patients with non-epidemic illnesses and to appropriate controls. By filling these gaps in our knowledge it should be possible to formulate rational prevention mechanisms for human gastrointestinal illnesses.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".