Digital versus analogue pleural drainage phase 1: prospective evaluation of interobserver reliability in the assessment of pulmonary air leaks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it