The impact of the COVID-19 pandemic on neuroimaging volume in New York City
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The COVID-19 pandemic acutely disrupted all facets of healthcare, with future implications that are expected to resonate for many years. We investigated the effect of the pandemic on neuroimaging volume, hypothesizing that all representative studies would experience a reduction in volume, with those typically performed in the inpatient setting (noncontrast enhanced CT head and CTA head/neck) taking longer to recover to pre-pandemic volumes compared to studies typically performed in the outpatient setting (MR brain with and without and MR lumbar spine without). MATERIALS AND METHODS: We retrospectively queried our institution's radiology reporting system to collect weekly data for 1 year following the World Health Organization declaration of a pandemic (11 March 2020-9 March 2021) and compared them to imaging volumes from the previous year (11 March 2019-9 March 2020). We subsequently analyzed quarterly data (e.g., first quarter comparison: 3/11/2020-6/9/2020 was compared to 3/11/2019-6/9/2019). RESULTS: All studies experienced decreased volume during the first quarter of the year following onset of the COVID-19 pandemic, with noncontrast enhanced CT head failing to recover to pre-pandemic volumes. CTA head/neck actually surpassed pre-pandemic volume by the second quarter of the year. MRI brain w/wo and MRI lumbar spine without recovered to baseline volume by the second quarter. CONCLUSION: Noncontrast enhanced CT head did not recover pre-pandemic imaging volume. CTA head/neck volume initially decreased, however volume increased above pre-pandemic levels during the second quarter; this finding may be attributable to a prothrombotic state in COVID-19 patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".