Prospective trial evaluating sonography after thoracic surgery in postoperative care and decision making
Bibliographic record
Abstract
OBJECTIVE: Following thoracic surgery, daily chest X-rays (CXRs) are performed to assess patient evolution and to make decisions regarding chest tube removal and patient discharge. Sonography after thoracic surgery (SATS) has the potential to be an effective, convenient, inexpensive and easy to learn tool in the post-operative management of thoracic surgery patients. We hypothesized that SATS could alleviate the need for repetitive CXRs, thus reducing the related risks, costs and inconvenience. METHODS: This study consisted of a prospective cohort trial. All patients scheduled to undergo thoracic surgery at a single academic medical centre were eligible. Post-operative bedside pleural ultrasound was performed whenever a CXR was ordered by the treating team. Investigators specifically assessed patients with the goals of identifying pleural effusions and pneumothoraces. Study investigators were blinded to CXR results. SATS findings were compared with CXRs, which were considered the gold standard in routine post-operative pleural space evaluation. RESULTS: One hundred and twenty patients were prospectively enrolled over a 5.5-month period. Three hundred and fifty-two ultrasound examinations were performed (mean = 3.0 ± 2.4 exams per patient). The time interval between the ultrasound and the comparative CXR was 166 ± 149 min. The mean time required to perform SATS was 11 ± 6 min per exam. In the detection of pleural effusion, SATS yielded a sensitivity of 83.1% and a specificity of 59.3%. In the detection of pneumothoraces, a sensitivity of 21.2% and a specificity of 94.7% were obtained. CONCLUSIONS: Post-operative ultrasound may alleviate the need to perform routine CXR in patients with a previously ruled out pneumothorax. SATS used selectively may be able to reduce the number of routine CXRs performed; however, it does not have high enough accuracy to replace CXRs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".