Imaging strategies for detection of urgent conditions in patients with acute abdominal pain: diagnostic accuracy study
Bibliographic record
Abstract
OBJECTIVE: To identify an optimal imaging strategy for the accurate detection of urgent conditions in patients with acute abdominal pain. DESIGN: Fully paired multicentre diagnostic accuracy study with prospective data collection. SETTING: Emergency departments of two university hospitals and four large teaching hospitals in the Netherlands. PARTICIPANTS: 1021 patients with non-traumatic abdominal pain of >2 hours' and <5 days' duration. Exclusion criteria were discharge from the emergency department with no imaging considered warranted by the treating physician, pregnancy, and haemorrhagic shock. INTERVENTION: All patients had plain radiographs (upright chest and supine abdominal), ultrasonography, and computed tomography (CT) after clinical and laboratory examination. A panel of experienced physicians assigned a final diagnosis after six months and classified the condition as urgent or non-urgent. MAIN OUTCOME MEASURES: Sensitivity and specificity for urgent conditions, percentage of missed cases and false positives, and exposure to radiation for single imaging strategies, conditional imaging strategies (CT after initial ultrasonography), and strategies driven by body mass index and age or by location of pain. RESULTS: 661 (65%) patients had a final diagnosis classified as urgent. The initial clinical diagnosis resulted in many false positive urgent diagnoses, which were significantly reduced after ultrasonography or CT. CT detected more urgent diagnoses than did ultrasonography: sensitivity was 89% (95% confidence interval 87% to 92%) for CT and 70% (67% to 74%) for ultrasonography (P<0.001). A conditional strategy with CT only after negative or inconclusive ultrasonography yielded the highest sensitivity, missing only 6% of urgent cases. With this strategy, only 49% (46% to 52%) of patients would have CT. Alternative strategies guided by body mass index, age, or location of the pain would all result in a loss of sensitivity. CONCLUSION: Although CT is the most sensitive imaging investigation for detecting urgent conditions in patients with abdominal pain, using ultrasonography first and CT only in those with negative or inconclusive ultrasonography results in the best sensitivity and lowers exposure to radiation.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".