COVID-19 Conceptual Modeling: Single-Center Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Conceptual models are abstract representations of the real world. They are used to refine medical and nonmedical health care scopes of service. During the COVID-19 pandemic, numerous analytic predictive models were generated aiming to evaluate the impact of implemented policies on mitigating the spread of the virus. The models also aimed to examine the psychosocial factors that might govern the general population's adherence to these policies and to identify factors that could affect COVID-19 vaccine uptake and allocation. The outcomes of these analytic models helped set priorities when vaccines were available and predicted readiness to resume non-COVID-19 health care services. OBJECTIVE: The objective of our research was to implement a descriptive-analytical conceptual model that analyzes the data of all COVID-19-positive cases admitted to our hospital from March 1 to May 31, 2020, the initial wave of the pandemic, the time interval during which local policies and clinical guidelines were constantly updated to mitigate the local effects of COVID-19, minimize mortality, reduce intensive care unit (ICU) admission, and ensure the safety of health care providers. The primary outcome of interest was to identify factors that might affect mortality and ICU admission rates and the impact of the implemented policy on COVID-19 positivity among health care providers. The secondary outcome of interest was to evaluate the sensitivity of the COVID-19 visual score, implemented by the Saudi Arabia Ministry of Health for COVID-19 risk assessment, and CURB-65 (confusion, urea, respiratory rate, blood pressure, and age >65 years) scores in predicting ICU admission or mortality among the study population. METHODS: This was a cross-sectional study. The relevant attributes were constructed based on research findings from the first wave of the pandemic and were electronically retrieved from the hospital database. Analysis of the conceptual model was based on the International Society for Pharmacoeconomics and Outcomes Research guidelines and the Society for Medical Decision-Making. RESULTS: A total of 275 individuals tested positive for COVID-19 within the study design interval. The conceptualization model revealed a low-risk population based on the following attributes: a mean age of 42 (SD 19.2) years; 19% (51/275) of the study population being older adults ≥60 years of age; 80% (220/275) having a CURB-65 score <4; 53% (147/275) having no comorbidities; 5% (13/275) having extreme obesity; and 20% (55/275) having a significant hematological abnormality. The overall rate of ICU admission for the study population was 5% (13/275), and the overall mortality rate was 1.5% (4/275). The multivariate correlation analysis revealed that a high-selectivity approach was adopted, resulting in patients with complex medical problems not being sent to MOH isolation facilities. Furthermore, 5% of health care providers tested positive for COVID-19, none of whom were health care providers allocated to the COVID-19 screening areas, indicating the effectiveness of the policy implemented to ensure the safety of health care providers. CONCLUSIONS: Based on the conceptual model outcome, the selectivity applied in retaining high-risk populations within the hospital might have contributed to the observed low mortality rate, without increasing the risk to attending health care providers.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".