The Association Between the Use of Low-Slice Computed Tomography Machines and Downstream Care: Comparative Study of 16-Slice and 64-Slice Computed Tomography Angiography
Bibliographic record
Abstract
BACKGROUND: Although computed tomography (CT) studies on machines with more slices have reported higher positive and negative predictive values, the impact of using low-slice (16-slice) CT machines on downstream testing has not been well studied. In community outpatient settings, low-slice CT machines remain in use, although many hospitals have adopted higher-slice machines. OBJECTIVE: This study examines the association between the use of low-slice CT machines and downstream invasive testing in the context of the CT angiography of the neck. METHODS: Included health insurance claims pertained to adults with commercial or Medicare Advantage health plans who underwent the CT angiography of the neck. Site certification data were used to assign counts of slices to claims. Claims that were made in the 60 days after CT were examined for cervicocerebral angiography. The association between the number of slices and cervicocerebral angiography was evaluated by using a chi-square test and multivariate logistic regression. RESULTS: Claims for 16-slice CT had a 5.1% (33/641) downstream cervicocerebral angiography rate, while claims for 64-slice CT had a 3.1% (35/1125) rate, and a significant difference (P=.03) was observed. An analysis that was adjusted for patient demographics also found a significant relationship (odds ratio 1.64, 95% CI 1.00-2.69; P=.047). CONCLUSIONS: The use of low-slice CT machines in the community may impact the quality of care and result in more downstream testing.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".