Performance of Early-Warning Scores in Predicting Mortality in an HIV-Infected Population with Sepsis in Uganda
Bibliographic record
Abstract
Early-warning scores (EWS) have the potential to improve resource allocation and hasten care in sub-Saharan Africa (SSA). Despite the high prevalence of HIV infection in SSA, current EWS do not take into account HIV serostatus. We conducted a retrospective study at Mbarara Regional Referral Hospital (MRRH) in Uganda to evaluate the performance of CRB-65, modified early-warning score (MEWS), quick sepsis-related organ failure assessment (qSOFA), rapid acute physiology score (RAPS), rapid emergency medicine score (REMS), South African triage scale (SATS), and shock index (SI) in predicting mortality among HIV-infected patients presenting with sepsis. We included patients admitted with sepsis to MRRH between January 2014 and December 2015 that had an HIV-positive serostatus and at least one valid heart rate, respiratory rate, systolic blood pressure, diastolic blood pressure, temperature, and oxygen saturation. Glasgow coma scale was imputed with the median. We used the area under the receiver operating curve (AUC) with tenfold cross-validation to assess the performance of each EWS. Of the 193 patients, the median (interquartile range) age was 34 (27, 42) years, 87 (45.0%) were female and 65 (44.6%) died. The AUC (95% confidence interval) was 0.53 (0.43, 0.62) for CRB65, 0.53 (0.44, 0.62) for MEWS, 0.57 (0.46, 0.68), for qSOFA, 0.60 (0.51, 0.69) for RAPS, 0.55 (0.46, 0.63) for REMS, 0.53 (0.45, 0.62) for SATS, and 0.54 (0.46, 0.63) for SI. The ability of EWS to predict mortality in an HIV-infected patient population with sepsis in Uganda was poor. EWS used in SSA should be derived from African patient populations and adjust for HIV serostatus. All authors: No reported disclosures.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".