Trends in Inpatient Utilization of Head Computerized Tomography Scans in the United States: A Brief Cross-Sectional Study
Bibliographic record
Abstract
Background Although computed tomography (CT) has revolutionized the field of medicine due to its incredible diagnostic capabilities, the trends regarding the usage of CT scans, especially in the field of neuroscience, are not very clear. We aim to find the trends in the usage of inpatient head CT scans in the United States using a robust database. Methods We queried the national inpatient usage of head CT scans in the United States from 1997 to 2014 using a robust national database. The trends in usage were analyzed based on age, gender, insurance types, and patients' income. Results During the study period, we recorded a total of 5,309,329 head CT scans, of which 51% were female. The total number of head CT scans in the United States dropped significantly from 527,026 cases to 181,095 cases (p=0.000). The decrease was with a steep slope from 1997 to 2002, and since then the decreasing slope turned to a steady state. The decrease in head CT scans was significant in all age groups (p = 0.001), more significant in uninsured payers (-79.4%, p=0.000), and prominent in low-income patients (-70.5 %, p=0.000). Conclusions Our study showed that national inpatient usage of CT scans of the head significantly decreased during the past two decades. This decrease is presumably multifactorial: reducing the number of unnecessary radiations, increased appropriateness audits by the government, payers' payment reductions, and integrated electronic platforms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".