Clinical Diagnosis of Bacterial Infection via FDG-PET Imaging
Bibliographic record
Abstract
The key challenge in the treatment of bacterial infection is rapid identification of bacteremia at an early stage of the diseases. Currently available imaging systems such as computed tomography (CT) and magnetic resonance imaging (MRI) can only detect bacterial infection after they have become systemic or have caused significant anatomical tissue damage, and at this stage infection are challenging to treat due to the high bacterial burden. To this day positron emission tomography (PET) imaging has showed the great potential for improving the diagnosis of bacterial infection because of the high sensitivity of PET radionuclides, capability of detecting molecular biology in details (even prior to anatomic change). Fluorodeoxyglucose (FDG) PET has been developed for bacterial imaging with incredible success. Whole body PET imaging with FDG for the diagnosis of bacterial infection and monitoring response to treatment has been well established. FDG-PET will not only help to accelerate the diagnosis of infection but improve the bacterial treatment. In this review, we focus on FDG-PET imaging for diagnosing bacterial infection in the clinic.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".