Increased rate of significant findings on brain MRI during the early stage of the COVID-19 pandemic
Bibliographic record
Abstract
Objectives To assess the effect of the COVID-19 pandemic on the proportion of abnormal paediatric neuroimaging findings as a surrogate marker for potential underutilisation. Methods Consecutive paediatric brain MRIs performed between March 27th and June 19th 2019 (T baseline ) and March 23rd and June 1st 2020 (T pandemic ) were reviewed and classified according to presence or absence and type of imaging abnormality, and graded regarding severity on a 5-point Likert scale, where grade 4 was defined as abnormal finding requiring non-urgent intervention and grade 5 was defined as acute illness prompting urgent medical intervention. Non-parametric statistical testing was used to assess for significant differences between T pandemic vs. T baseline . Results Fewer paediatric MRI brains were performed during T pandemic compared to T baseline (12.2 vs 14.7 examinations/day). No significant difference was found between the two time periods regarding sex and age (T baseline : 557 females (44.63%), 7.95 ± 5.49 years, T pandemic : 385 females (44.61%), 7.64 ± 6.11 years; p = 1 and p = .079, respectively). MRI brain examinations during T pandemic had a higher likelihood of being abnormal, 41.25% vs. 25.32% ( p<.0001). Vascular abnormalities were more frequent during T pandemic (11.01% vs 8.01%, p = .02), congenital malformations were less common (8.34% vs 12.34%, p = .004). Severity of MRI brain examinations was significantly different when comparing group 4 and group 5 individually and combined between T baseline and T pandemic ( p = .0018, p < .0001, and p <.0001, respectively). Conclusions The rate of abnormality and severity found on paediatric brain MRI was significantly higher during the early phase of the pandemic, likely due to underutilisation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".