Response to <i>Pseudomonas aeruginosa</i> pre‐septal cellulitis and bacteremia in a pediatric oncology patient
Bibliographic record
Abstract
To the Editor: Milstone et al. 1 have presented an illustrative case of a now uncommon infection caused by an important pathogen that is, however, a very uncommon cause of bacteremic preseptal cellulitis. Two points about this case should be emphasized. First, any child with cancer who develops fever and periorbital swelling should have an emergency CT scan of the orbits, paranasal sinuses, and adjacent intracranial region to look for the extent of disease in these structures. There is no value to plain X-rays in this setting. Second, it is worthwhile to consider the pathogenesis of this infection. Bacteremic preseptal cellulitis is presumed to be caused by local hematogenous dissemination from a portal of entry in the nasopharynx, rather than by dissemination from some distant site 2. The most common causes are Haemophilus influenzae type b and Streptococcus pneumoniae, which both typically colonize the nasopharynx 2. With the success of universal immunization program against both of these pathogens, bacteremic preseptal cellulitis is now much less common. Although Pseudomonas aeruginosa predominantly colonizes gastrointestinal tract, it may colonize other body sites, including the nasopharynx 3.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".