PP172 Topic: AS15–Lung: Respiratory Support/Acute Respiratory Failure/Other: CLINICAL CHARACTERISTICS AND OUTCOMES OF PICU ADMISSIONS FOR ACUTE RESPIRATORY INFECTION PRECEDING AND FOLLOWING COVID-19 IN BRITISH COLUMBIA, CANADA: A RETROSPECTIVE COHORT STUDY INCLUDING READAPT-KIDS.
Bibliographic record
Abstract
Aims & Objectives: Pediatric Intensive Care Unit (PICU) admissions for children with acute respiratory illness (ARI) decreased following the onset of COVID-19. With lifting of public health measures, pediatric hospitals reported a resurgence of ARI requiring hospitalization. We aim to describe clinical characteristics, referral patterns, and hospital outcomes of children admitted to BC Children’s Hospital PICU (Canada) due to ARI (1) Prior to COVID-19 (July 1st, 2018-June 30th, 2019), (2) During COVID-19-related public health measures (July 1st, 2020-June 30th, 2021), and (3) Following lifting of public health measures (July 1st, 2022-June 30th, 2023). We hypothesize that, compared to prior years, during the 2022-2023 season, (1) there was an increased monthly incidence of PICU admissions for ARI and (2) patients had greater illness severity at admission and fewer organ-support free days (OFDs) at 30 days. Methods: Retrospective cohort study including all patients aged 0-17 years admitted to BCCH PICU with an ARI over the stated time periods. Results: We will summarize patient characteristics, transport requirements, and hospital outcomes by time period using descriptive statistics. We will compare monthly incidence rates of PICU admission across three time periods and conduct Poisson regression analysis to evaluate the association between timing of admission and OFDs at 30 days, after adjusting for patient-level covariates. Conclusions: This research will contribute to improved understanding of the epidemiology of ARI-related PICU admissions following onset of the COVID-19 pandemic in Canada and may inform future health system planning and resource allocation. Keywords: TRANSPORT, acute respiratory infection, COVID-19, PICU
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".