Systematic Review, Critical Appraisal, and Analysis of the Quality of Economic Evaluations in Stroke Imaging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: This study reviews the quality of economic evaluations of imaging after acute stroke and identifies areas for improvement. METHODS: We performed full-text searches of electronic databases that included Medline, Econlit, the National Health Service Economic Evaluation Database, and the Tufts Cost Effectiveness Analysis Registry through July 2012. Search strategy terms included the following: stroke*; cost*; or cost-benefit analysis*; and imag*. Inclusion criteria were empirical studies published in any language that reported the results of economic evaluations of imaging interventions for patients with stroke symptoms. Study quality was assessed by a commonly used checklist (with a score range of 0% to 100%). RESULTS: Of 568 unique potential articles identified, 5 were included in the review. Four of 5 articles were explicit in their analysis perspectives, which included healthcare system payers, hospitals, and stroke services. Two studies reported results during a 5-year time horizon, and 3 studies reported lifetime results. All included the modified Rankin Scale score as an outcome measure. The median quality score was 84.4% (range=71.9%-93.5%). Most studies did not consider the possibility that patients could not tolerate contrast media or could incur contrast-induced nephropathy. Three studies compared perfusion computed tomography with unenhanced computed tomography but assumed that outcomes guided by the results of perfusion computed tomography were equivalent to outcomes guided by the results of magnetic resonance imaging or noncontrast computed tomography. CONCLUSIONS: Economic evaluations of imaging modalities after acute ischemic stroke were generally of high methodological quality. However, important radiology-specific clinical components were missing from all of these analyses.
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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 | 0.352 | 0.730 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.017 |
| Bibliometrics | 0.028 | 0.021 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".