Infectious diseases specialist management improves outcomes for outpatients diagnosed with cellulitis in the emergency department: a double cohort study
Bibliographic record
Abstract
Three hospital emergency rooms (ERs) routinely referred all cases of cellulitis requiring outpatient intravenous antibiotics, to a central ER-staffed cellulitis clinic. We performed a retrospective cohort study of all patients seen by the ER clinic in the last 4months preceding a policy change (ER management cohort [ERMC]) (n=149) and all those seen in the first 3months of a new policy of automatic referral to an infectious disease (ID) specialist-supervised cellulitis clinic (ID management cohort [IDMC]) (n=136). Fifty-four (40%) of 136 patients in the IDMC were given an alternative diagnosis (noncellulitis), compared to 16 (11%) of 149 in the ERMC (P<0.0001). Logistic regression-demonstrated rates of disease recurrence were lower in the IDMC than the ERMC (hazard ratio [HR], 0.06; P=0.003), as were rates of hospitalization (HR, 0.11; P=0.01). There was no significant difference in mortality. Automatic ID consultation for cellulitis was beneficial in differentiating mimickers from true cellulitis, reducing recurrence, and preventing hospital admissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".