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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".