Multimodal treatment for acute empyema based on the patient’s condition, including patients who are bedridden: A single center retrospective study
Bibliographic record
Abstract
BACKGROUND: The incidence of acute empyema has increased in various countries; some elderly patients with acute empyema have contraindications for surgery under general anesthesia. Therefore, suitable management based on a patient's clinical condition is required. METHODS: We evaluated the different surgical and nonsurgical therapeutic approaches available for patients with acute empyema. This was a retrospective study of 57 patients with acute empyema who received treatment in our department between May 2015 and February 2019. For patients who did not initially improve with drainage or drainage combined with fibrinolytic therapy, surgery, or additional percutaneous drainage was performed based on their general condition. We compared several clinical factors pertaining to the patients who underwent surgical versus nonsurgical treatment. RESULTS: Our study showed that the patients with a performance status of 0-2 and an American Society of Anesthesiologists physical status classification of class II or lower underwent surgery safely without major operative complications. The combination of repeated drainage of the pleural cavity and fibrinolytic therapy appeared to be a reasonable nonsurgical management option for patients in poor overall condition. CONCLUSION: For an aging population, we think that the combination of repeated pleural cavity drainage procedures and fibrinolytic therapy is a reasonable nonsurgical strategy for the management of patients with acute empyema.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".