Innovations in Data-Driven Approaches to Improve Sepsis Care for Children in Low-Resource Settings (presentations) ~ QI
Bibliographic record
Abstract
In November 2022, the Centre for International Child Health (CICH) at BC Children's Hospital brought together leading young researchers at the 2022 American Society for Tropical Medicine and Hygiene Annual Meeting to share their work developing leveraging frugal digital technology and data-driven vulnerability prediction to improve care across different points of a critically-ill child’s journey. Panelists described development and validation of risk prediction models, highlighting how such models can guide targeted, affordable interventions. Attendees had a chance to learn how practical and affordable digital technology equipped with clinical algorithms can support health workers with identifying, treating, and following-up high-risk children, improving outcomes within health facilities and at home, and the role of promising new host and pathogen biomarkers in enhancing the predictive capability of prognostic models. Speakers: Arjun Chandna: Risk stratification of pediatric febrile illness to inform effective community management and earlier referral to higher-level care. Samuel Akech: Smart Triage, a prediction-based facility quality improvement program for critically ill children. Teresa Kortz: Point-of-care biomarkers for mortality risk stratification in pediatric severe febrile illness; using next-generation sequencing for etiology of severe febrile illness Matthew Wiens: Smart Discharges, post-discharge interventions based on the risk of post-discharge mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".