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Record W3126162611 · doi:10.21037/jtd-20-2993

Clinical application of a digital thoracic drainage system for objectifying and quantifying air leak versus the traditional vacuum system: a retrospective observational study

2021· article· en· W3126162611 on OpenAlexaff
Song Am Lee, Jun Seok Kim, Hyun Keun Chee, Jae Joon Hwang, Michael Ji, Yo Han Kim, Hyeong Ju Moon, Woo Surng Lee

Bibliographic record

VenueJournal of Thoracic Disease · 2021
Typearticle
Languageen
FieldMedicine
TopicPleural and Pulmonary Diseases
Canadian institutionsUniversity of Calgary
FundersKonkuk University
KeywordsMedicineThoracostomyPneumothoraxSurgeryDrainageChest tubeObservational studyPleural cavityInternal medicine

Abstract

fetched live from OpenAlex

Background: Digital thoracic drainage systems have recently been introduced and widely used in clinical practices in developed countries. These systems can monitor intrathoracic pressure changes and air leaks in real time, and also allow for objective and quantitative analyses, which aid in managing patients with a prolonged persistent air leak into the pleural space. We investigated the feasibility and effectiveness of such a new device versus the traditional vacuum system for treating patients with pneumothorax. Methods: Closed thoracostomy drainage was carried out on 100 adult patients with primary or secondary pneumothorax between January 2017 and December 2018. All the patients were aged ≥18 years and treated with a chest tube at a single medical center by the same cardiothoracic surgeons and intensivists. Patients who underwent closed thoracostomy drainage using an indwelling 24-French chest tube were divided into 2 groups immediately before closed thoracostomy: the digital thoracic drainage group (digital group, n=50) and the traditional analogue thoracic drainage group (analogue group, n=50). The detailed information about demographic data, treatment outcome, duration of indwelling catheterization., hospital days, cost-effectiveness and patient satisfaction was evaluated. We also evaluated whether digitally recorded intrapleural pressure changes and air leaks would predict chest tube removal timing and outcome. Results: The baseline parameters of the 2 groups were comparable with no significant differences in sex, age, weight or body mass index. The mean hospital day was shorter in the digital group than in the analogue group (17.96±12.23 vs. 18.32±16.64, P=0.902), and there was no statistically significant difference in the hospital length of stay between the 2 groups. Air leaks through the chest tube and duration of chest tube indwelling hours showed no significant statistical differences between the digital and analogue groups (213.47±219.80 vs. 261.94±184.47, P=0.235 and 223.44±218.75 vs 275.29±186.06, P=0.205, respectively). Total drainage amount and ambulation time per day were significantly higher in the digital group than in the analogue group [209.62±139.63 vs. 162.48±80.42 (P=0.042) and 6.42±3.62 vs.3.94±1.74 (P<0.001), respectively]. Hours of full expansion were significantly shorter and sleep disturbance caused by the noise of chest tube drainage was less in the digital group than in the analogue group [25.64±14.55 vs. 46.52±25.53 (P<0.001) and 2.38±1.03 vs. 5.70±2.87 (P<0.001), respectively]. Conclusions: To date, there is no definite consensus and guidelines on the standardized digital suction system in pneumothorax. This study proposed the guidelines for the application of digital thoracic drainage systems in pneumothorax and also suggested that digital thoracic drainage systems might be a valuable tool to determine chest tube removal timing and reducing the length of hospital stay in patients with pneumothorax.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.216
GPT teacher head0.428
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2021
Admission routes1
Has abstractyes

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