Reduction of cardiac imaging tests during the COVID-19 pandemic: The case of Italy. Findings from the IAEA Non-invasive Cardiology Protocol Survey on COVID-19 (INCAPS COVID)
Bibliographic record
Abstract
BACKGROUND: In early 2020, COVID-19 massively hit Italy, earlier and harder than any other European country. This caused a series of strict containment measures, aimed at blocking the spread of the pandemic. Healthcare delivery was also affected when resources were diverted towards care of COVID-19 patients, including intensive care wards. AIM OF THE STUDY: The aim is assessing the impact of COVID-19 on cardiac imaging in Italy, compare to the Rest of Europe (RoE) and the World (RoW). METHODS: A global survey was conducted in May-June 2020 worldwide, through a questionnaire distributed online. The survey covered three periods: March and April 2020, and March 2019. Data from 52 Italian centres, a subset of the 909 participating centres from 108 countries, were analyzed. RESULTS: In Italy, volumes decreased by 67% in March 2020, compared to March 2019, as opposed to a significantly lower decrease (p < 0.001) in RoE and RoW (41% and 40%, respectively). A further decrease from March 2020 to April 2020 summed up to 76% for the North, 77% for the Centre and 86% for the South. When compared to the RoE and RoW, this further decrease from March 2020 to April 2020 in Italy was significantly less (p = 0.005), most likely reflecting the earlier effects of the containment measures in Italy, taken earlier than anywhere else in the West. CONCLUSIONS: The COVID-19 pandemic massively hit Italy and caused a disruption of healthcare services, including cardiac imaging studies. This raises concern about the medium- and long-term consequences for the high number of patients who were denied timely diagnoses and the subsequent lifesaving therapies and procedures.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".