PP343 Topic: AS09–Global Health/Resource Limited Setting/Health Inequalities/Impact of Global Warming/Other: RISK-STRATIFIED DISCHARGE CARE FOR CHILDREN WITH SUSPECTED SEPSIS REDUCES ALL-CAUSE 6-MONTH POST-DISCHARGE MORTALITY
Bibliographic record
Abstract
Aims & Objectives: Mortality following discharge is common in low-income country (LIC) settings. Innovative approaches to address pediatric post-discharge mortality in LICs are urgently needed. Methods: We conducted a prospective parallel cluster crossover trial at 6 hospitals in Uganda. Children <60 months admitted due to suspected infectious illness were eligible for enrollment. Phase 1 was a comparative control. During phase 2, enrolled children were screened for post-discharge mortality risk at admission using a multivariable risk algorithm. All children received counselling on post-discharge care practices during admission and at discharge. High-risk children received referrals and automated SMS engagement at 2, 7 and 14 days at a clinic of their choice, or by a community health worker. Survival analysis, adjusting for age, sex, site, period time and predicted risk of mortality was used to estimate the effect of the intervention on 6-month all-cause post-discharge mortality. Results: 12169 subjects were enrolled (phase 1: n=6073; phase 2: n=6096). Baseline characteristics were similar between groups. The median age was 0.8 months (IQR: 0.2-1.7), with 56% of participants male. The multivariable risk algorithm gave a mean predicted risk of post-discharge mortality of 6.1% in phase 1 and 5.9% in phase 2, while the observed mortality rate was 6.0% in phase 1 and 4.9% in phase 2, respectively. The adjusted HR of the intervention was 0.77 (95% CI 0.66-0.90).Conclusions: Improved discharge counselling, alongside a risk-stratified approach to follow-up care can improve post-discharge survival. Such strategies can be incorporated into national programs to address this common cause of child mortality. Keywords: Post-discharge Mortality, pediatrics, Sepsis, risk prediction
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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".