Cost‐Effective Diagnosis of Ingested Foreign Bodies
Bibliographic record
Abstract
OBJECTIVES: To compare the cost effectiveness of plain film radiography, computed tomography (CT), and endoscopy as initial diagnostic modalities in adult patients complaining of retained ingested foreign bodies. DESIGN: A systematic literature review was conducted to determine key statistics for the analysis, such as prevalence of disease, prevalence of complications, and the sensitivity and specificity of each diagnostic modality. Costs were estimated using 2006 Medicare reimbursement for hospital and professional fees. A deterministic cost-effectiveness analysis was then conducted using decision analysis software and a decision tree model to evaluate the various diagnostic strategies. After identifying initial results, we also performed sensitivity and threshold analysis to assess the strength of the recommendations. RESULTS: We reviewed 316 abstracts, identified 16 pertinent studies that included a total of 7,088 patients with possible foreign bodies, and extracted key statistics from those papers. Decision analysis showed that CT scanning as an initial diagnostic strategy proved more cost effective than plain film or operative endoscopy. The incremental cost of immediate endoscopy for every additional correctly diagnosed patient was $5,238. Plain radiography was more costly and less effective, even with the addition of confirmatory CT scanning after a negative plain film. Sensitivity and threshold analyses demonstrated that these results are robust. CONCLUSIONS: Patients presenting with a complaint of a retained ingested foreign body are most cost-effectively managed with CT scan, after history and physical. Immediate endoscopy may be considered if CT is not available, although it adds significant cost. Plain films are dominated by these two diagnostic strategies.
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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 | 0.009 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".