(F)utility of computed tomography of the chest in the presence of pleural effusion
Bibliographic record
Abstract
BACKGROUND: Pleural effusion is common and can cause significant morbidity. The chest X-ray is often the initial radiological test, but additional tests may be required to reduce uncertainty and to provide additional diagnostic information. However, additional exposure and unnecessary costs should be prevented. The objective of the study was to assess the clinical benefit of an additional chest computed tomography (CT) scan over plain chest X-ray alone in the management of patients with pleural effusion. METHODS: Retrospective analysis in 94 consecutive patients with pleural effusion who underwent chest X-ray and CT scan over an 18-month period in a single institution. All chest X-ray and CT scan reports were compared and correlated with clinical parameters in order to assess their utility in the clinical management. No blinding was applied. RESULTS: In 75 chest CT scan reports (80 %), information provided by the radiologist did not change clinical management when compared to plain chest X-ray alone and did not provide any additional information over chest X-ray. Only 2/49 (4 %) of the native chest CT scan reports provided clinically relevant information as compared to 17/45 (38 %) contrast-enhanced chest CT scan reports (p<0.001). CONCLUSIONS: In this retrospective cohort of patients with pleural effusion, an additional chest CT scan was not useful in the majority of patients. However, if a chest CT scan is required, then a contrast-enhanced study after pleural aspiration should be performed. Further prospective studies are required to confirm these findings.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".