Bacillary Angiomatosis in an HIV‐positive Man with Multiple Risk Factors: A Clinical and Epidemiological Puzzle
Bibliographic record
Abstract
A 36-year-old man with advanced AIDS presented with skin lesions, fever and weight loss. Physical examination revealed skin lesions suggestive of bacillary angiomatosis. Diagnostic imaging identified bone lesions, soft tissue masses and a single hepatic lesion. His clinical presentation was most consistent with Bartonella quintana; however, his risk for infection with B quintana was limited to a remote, brief period of homelessness. In contrast, his frequent contact with cats presented substantial potential exposure to Bartonella henselae.Diagnosing a Bartonella infection presents challenges. The detection of bacteremia is hindered by slow growth in BACTEC media (BD Diagnostic Systems, Canada). In the present case, recovery of the organism required blind subcultures after at least eight days of incubation. Speciation by 16s ribosomal RNA analysis confirmed B quintana as the pathogen.Because the patient presented with skin lesions, the diagnosis was straightforward; however, skin lesions are nondiagnostic in some cases. Microbiological confirmation of the diagnosis rests on sufficient clinical suspicion to apply labour-intensive laboratory protocols.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".