European Society of Thoracic Surgeons electronic quality of life application after lung resection: field testing in a clinical setting
Bibliographic record
Abstract
OBJECTIVES: Technology has the potential to assist healthcare professionals in improving patient-doctor communication during the surgical journey. Our aims were to assess the acceptability of a quality of life (QoL) application (App) in a cohort of cancer patients undergoing lung resections and to depict the early perioperative trajectory of QoL. METHODS: This multicentre (Italy, UK, Spain, Canada and Switzerland) prospective longitudinal study with repeated measures used 12 lung surgery-related validated questions from the European Organisation for Research and Treatment of Cancer Item Bank. Patients filled out the questionnaire preoperatively and 1, 7, 14, 21 and 28 days after surgery using an App preinstalled in a tablet. A one-way repeated measures analysis of variance was run to determine if there were differences in QoL over time. RESULTS: A total of 103 patients consented to participate in the study (83 who had lobectomies, 17 who had segmentectomies and 3 who had pneumonectomies). Eighty-three operations were performed by video-assisted thoracoscopic surgery (VATS). Compliance rates were 88%, 90%, 88%, 82%, 71% and 56% at each time point, respectively. The results showed that the operation elicited statistically significant worsening in the following symptoms: shortness of breath (SOB) rest (P = 0.018), SOB walk (P < 0.001), SOB stairs (P = 0.015), worry (P = 0.003), wound sensitivity (P < 0.001), use of arm and shoulder (P < 0.001), pain in the chest (P < 0.001), decrease in physical capability (P < 0.001) and scar interference on daily activity (P < 0.001) during the first postoperative month. SOB worsened immediately after the operation and remained low at the different time points. Worry improved following surgery. Surgical access and forced expiratory volume in 1 s (FEV1) are the factors that most strongly affected the evolution of the symptoms in the perioperative period. CONCLUSIONS: We observed good early compliance of patients operated on for lung cancer with the European Society of Thoracic Surgeons QoL App. We determined the evolution of surgery-related QoL in the immediate postoperative period. Monitoring these symptoms remotely may reduce hospital appointments and help to establish early patient-support programmes.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".