Digital versus analogue pleural drainage phase 1: prospective evaluation of interobserver reliability in the assessment of pulmonary air leaks
Bibliographic record
Abstract
OBJECTIVES: The ability to accurately characterize a pulmonary air leak is an essential skill in chest medicine and surgery. The objective was to evaluate interobserver variability in air leak assessments using analogue and digital pleural drainage systems. METHODS: Air leak severity in lung resection patients with a pulmonary air leak was prospectively evaluated by at least one thoracic surgeon, one surgical resident and one to two nurses using a standardized questionnaire. The first assessment was performed with pleural drains connected to an analogue system. Subsequently, patients were re-assessed after changing from the analogue to a digital drainage system. The thoracic surgeon's evaluation was considered the reference standard for comparison. Agreement between observers was quantified using the kappa (κ) statistic. RESULTS: A total of 128 air leak evaluations were completed in 30 patients (thoracic surgeon = 30; nurses = 56; resident = 30; physiotherapists = 12). The mean time between analogue and digital assessment was 2.16 (±1.66) h. The level of observer agreement regarding air leak severity significantly increased from very slight to substantial when using the digital drainage system [analogue κ = 0.03; confidence interval (CI): 0.04-0.11; P = 0.40) (digital κ = 0.61; CI: 0.49-0.73; P < 0.01]. Similar improvements were observed in subgroups of health-care professionals using digital technology. CONCLUSIONS: Digital pleural drainage technology improves the agreement level between members of the health-care team when assessing the severity of a pulmonary air leak after lung resection.
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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 | 0.031 | 0.055 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".