Lung Volume Reduction: streamlining time-to-intervention.
Bibliographic record
Abstract
Introduction: The mean time from referral for Lung Volume Reduction (LVR) to intervention in the West of Scotland exceeds one year. Despite demonstrable improvements in FEV1 and symptom-burden post intervention, the referral process remains protracted. This pilot study aimed to assess causes of delay and investigate streamlining strategies. Methods: A database of all patients referred to the LVR multidisciplinary team between December 2019 and January 2024 was analysed. Results: Of 69 referred patients (median Glenfield score: 3, mean age: 64 years, 62% male), the median (IQR) time from MDT discussion to echocardiography was 300 (129–588) days. The median time from pulmonary function tests (PFT) to cardiothoracic referral was 221 (96–393) days, and from cardiothoracic referral to LVR was 180 (134–370) days. Fourteen (20%) patients proceeded to intervention. Among 53 patients who underwent echocardiography, 7 had mild-to-severe LVSD and 1 had RVSD. An NT-proBNP >400 ng/L predicted mild or greater ventricular systolic dysfunction with 33% sensitivity, 100% specificity, 100% positive predictive value (PPV), and 82% negative predictive value (NPV) (n=12). Among 38 patients whom underwent blood gas analysis, 16 had evidence of hypercapnic respiratory failure. A serum bicarbonate >28 mmol/L predicted an arterial PCO2 >6 kPa with 100% sensitivity, 90% specificity, 89% PPV, and 100% NPV (n=37). Conclusion: Patients with an NT-proBNP <400 pg/mL may not require echocardiography before LVR (NPV 82%). Similarly, a normal serum bicarbonate level may obviate the need for arterial blood gas analysis (NPV 100%). Implementing these criteria could reduce LVR waiting times. Prospective data collection is ongoing.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".