205 Diagnosis of Sepsis in Steven’s-Johnson Syndrome and Toxic Epidermal Necrolysis
Bibliographic record
Abstract
Standard criteria used to diagnose sepsis in patients with burns does not necessarily apply to the SJS/TEN population in clinical settings. Therefore, the goal of this study was to review all SJS/TEN admissions to a provincial burn centre and assess the number of patients that had a diagnosis of sepsis using the Sepsis-3 guidelines. We included all patients admitted with a confirmed diagnosis of SJS/TEN to our burn centre from 2006–2018. Outcomes included a confirmed diagnosis of sepsis as defined by the sepsis-3 criteria, time to antibiotics, complications during hospital stay, length of stay, and mortality. A total of 42 patients were included. Mean age was 53 ± 20 years, 15 (36%) were male, and patients presented within median 8 (4–14) days of initial presentation of symptoms. The average SCORe of Toxic Epidermal Necrosis (SCORTEN) Scale on admission was 4.4 ± 1.6. Fifteen (36%) of patients had clinical documentation of sepsis. Of those with clinically documented sepsis, 80% had a positive blood culture. In the non-septic 27 patients, 33% had a positive blood culture. Nine patients (20%) died in hospital. Clinical diagnosis of sepsis in the SJS/TEN population is complicated by similarities in the clinical manifestation of infection. Use of definitions like the sepsis-3 criteria could facilitate consistency amongst clinicians and centres. The onset of sepsis and time to treatment may be impacted by increased awareness of the early signs and symptoms of SJS/TEN itself and transfer to a specialized referral centre.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".