18F-FDG PET/CT as a central tool in the shift from chronic Q fever to Coxiella burnetii persistent focalized infection
Bibliographic record
Abstract
Because Q fever is mostly diagnosed serologically, localizing a persistent focus of Coxiella burnetii infection can be challenging. F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) could be an interesting tool in this context.We performed a retrospective study on patients diagnosed with C burnetii infection, who had undergone F-FDG PET/CT between 2009 and 2015. When positive F-FDG PET/CT results were obtained, we tried to determine if it changed the previous diagnosis by discovering or confirming a suspected focus of C burnetii infection.One hundred sixty-seven patients benefited from F-FDG PET/CT. The most frequent clinical subgroup before F-FDG PET/CT was patients with no identified focus of infection, despite high IgG1 serological titers (34%). For 59% (n = 99) of patients, a hypermetabolic focus was identified. For 62 patients (62.6%), the positive F-FDG PET/CT allowed the diagnosis to be changed. For 24 of them, (38.7%), a previously unsuspected focus of infection was discovered. Forty-two (42%) positive patients had more than 1 hypermetabolic focus. We observed 21 valvular foci, 34 vascular foci, and a high proportion of osteoarticular localizations (n = 21). We also observed lymphadenitis (n = 27), bone marrow hypermetabolism (n = 11), and 9 pulmonary localizations.We confirmed thatF-FDG PET/CT is a central tool in the diagnosis of C burnetii focalized persistent infection. We proposed new diagnostic scores for 2 main clinical entities identified using F-FDG PET/CT: osteoarticular persistent infections and lymphadenitis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".