61 The distribution of MeMed BV assay results in Children hospitalized at McMaster Children’s Hospital with Severe community-acquired pneumonia (DISCOTHEQUE)
Bibliographic record
Abstract
Abstract Background Community-acquired pneumonia (CAP) is a leading cause of paediatric hospitalization. Most CAP in preschoolers is viral, but almost all children hospitalized with CAP are treated with antibiotics – this is a key stewardship gap. Commonly-used biomarkers do not reliably distinguish bacterial from viral pneumonia. However, a novel combination biomarker assay (MeMed BV, MBV) appears to have better discriminatory ability. Objectives To describe the MBV distribution in children hospitalized with CAP at a children’s hospital. Design/Methods A prospective cohort study at a tertiary center enrolling children aged 3+ months hospitalized with CAP. Data abstracted included demographics, presenting signs and symptoms, and clinical outcomes. Participants had MBV testing but results were not available to treating clinicians. Results 98 patients were enrolled, of whom 67 had results available. The mean age of participants was 6.24 years (SD=4.66) and all were treated with antibiotics. A significant proportion (40%) of participants had MBV results suggestive of viral infection (table 1). There was no association between having a positive nasopharyngeal swab and MBV result (p=0.68). 24 of 27 patients who were identified as at least moderate likelihood of viral infection had consolidation or pneumonia reported on their chest x-ray. Conclusion Use of the MBV assay may permit better identification of children hospitalized with severe CAP who are likely to have primary viral disease, for whom antibiotics may offer more potential harm than benefit. Our results will be critical to plan future intervention studies leveraging the MBV assay to better design management algorithms to optimize stewardship.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".