Effect of COVID-19 on the Number of CT-scans and MRI Services of Public Hospitals in Iran: An Interrupted Time Series Analysis
Bibliographic record
Abstract
Background: In February 2020, the Ministry of Health and Medical Education in Iran announced the first case of COVID-19. The aim of this study was to investigate the impact of COVID-19 on the number of CT-Scans and MRI services in public hospitals in western Iran. Methods: We collected CT-scans and MRI services data from 18 public hospitals via Vice-Chancellor Office, Lorestan University of Medical Sciences from January 2017 to February 2021. Interrupted time series analysis (ITSA) was conducted to assess the impact of COVID-19 on CT-Scans and MRI services. More specifically, ITSA was conducted using ordinary least squares regression with the number of CT-Scans and MRI services per 1,000 registered persons per month as dependent variable. Results: At the beginning of the observation period, the monthly rate of CT-Scans was constant (p for trend = 0.267) at 291.9 (from 95%CI 240.5 to 343.4) per 1,000 registered patients. The first case of COVID-19 coincided with an abrupt increase by 211.8 (from 95%CI 102.9 to 320.7) per 1,000 patients. Thereafter, the trend of CT-Scans did not change (p=0.576) compared to the pre-pandemic period. The rate of MRI services was 363.5 per 1,000 per registered patients per month (P = <0.0001) with a slightly decreasing trend (coefficient=-5; 95%CI, -6.9 to -3.1). Conclusion: The findings of this study showed that crises such as COVID-19 can affect the service delivery process. Health policymakers and decision makers should work to prevent potential reductions in health care during events such as COVID-19.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".