Replacing post–chest tube removal chest radiographs with clinical assessment in adult thoracic surgery patients: A single-center prospective study
Bibliographic record
Abstract
Objective: The necessity and utility of chest radiographs in the absence of clinical symptoms have been questioned after chest tube removal. This study aimed to evaluate the impact of replacing routine chest radiographs after chest tube removal with clinical observation on outcomes in patients undergoing elective thoracic surgery. Methods: This was a single-center prospective study of adult patients undergoing elective lung resection. Standard chest radiographs after chest tube removal were replaced with a clinical observation protocol for 2 hours after removal. Chest radiographs after chest tube removal were meant to be obtained only for symptomatic patients. The primary outcome was the incidence of adverse events related to this change. Secondary outcomes included changes in clinical management, length of stay, and postoperative complications. Results: < .05). Conclusions: Clinical observation can safely replace routine chest radiographs after chest tube removal in asymptomatic patients undergoing elective thoracic surgery. This approach may lead to shorter hospital stays and reduced healthcare costs without compromising patient safety. The findings support a clinically driven use of postoperative imaging in this patient population, highlighting the importance of individualized patient care.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".