Evaluation of a pediatric post-acute sequelae of SARS-CoV-2 index score
Bibliographic record
Abstract
Objective This study aims to assess the performance of the Researching COVID-19 to Enhance Recovery (RECOVER) initiative's proposed post-acute sequelae of COVID-19 (PASC) index in a cohort of children evaluated for SARS-CoV-2 infection, 6–12 months after exposure. Study design We conducted a multicenter, prospective cohort study with 6- and 12-month follow-up in 14 Canadian tertiary-care pediatric emergency departments (EDs) in the Pediatric Emergency Research Canada network. Eligible children were 6 to <18 years of age who were tested for acute SARS-CoV-2 infection. We assessed the score validity and reliability and evaluated the associations between PASC index scores dichotomized using threshold values (≥5.5 for ages 6 to <12 years and ≥5.0 for ages 12 to <18 years) and SARS-CoV-2 infection. Results Participants included 785 children, with a median age of 9 years (IQR: 7–13), enrolled between August 2020 and February 2022. Factor analysis identified characteristics that accounted for 32%–40% of variance. Strong correlations were identified between PASC index scores and PedsQL™ and overall health status; Cronbach's α ranged from 0.49 to 0.67. Changes in PASC index scores across time points accounted for 71% (6 to <12 years) and 63% (12 to <18 years) of total variance. The proportion of children exceeding PASC index score thresholds did not differ between children positive and negative for SARS-CoV-2 test in the 6 to <12 (25% vs. 22%; aOR: 1.2; 95% CI: 0.6, 2.5) and 12 to <18 (18% vs. 10%; aOR: 2.2; 95% CI: 0.5, 10.4) age groups at 6 months. Similar results were reported at 12 months. Conclusions While scores correlated with quality of life and overall health, internal reliability was low to acceptable. The PASC index was not associated with previous SARS-CoV-2 infection.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".