How safe is it to discharge home patients with a chest tube in place? A narrative review of the literature
Bibliographic record
Abstract
OBJECTIVES: In a time of particular focus on healthcare costs and quality metrics, combined with widespread embracing of the Early Recovery After Surgery approach, an outpatient setting for the management of prolonged air leak or excessive fluid drainage appears to be an acceptable option. The aim of this review is to evaluate the safety, efficacy and financial benefit of discharging home patients with chest tube after lung surgery or following chest drain insertion due to a pneumothorax. METHODS: We reviewed the current literature analysing all available full-text papers published in English (PubMed, Cochrane and EMBASE databases). Data were reported as descriptive narrative. RESULTS: Our findings show that discharging home patients with chest tube in situ has not only a positive impact on length of stay but it also seems to be cost-effective. In our literature review, contrasting results have emerged regarding readmission rates and development of complications, especially empyema. CONCLUSIONS: Thus, outpatients management of patients discharged with a chest drain is feasible and cost-effective. A standardization of follow-up with dedicated ambulatory setting might improve patients' safety and increase this practice amongst thoracic surgery institutions.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".