Postoperative Patient-Reported Outcomes after Uniportal Video-Assisted Thoracoscopic Surgery Using the Perioperative Symptom Assessment for Lung Surgery Scale
Bibliographic record
Abstract
This study aimed to use a new special inventory for lung surgery patients to evaluate postoperative symptoms and functional status and to identify factors that may affect these after uniportal video-assisted thoracoscopic surgery (VATS). In this single-center longitudinal cohort observational study, we used a new scale, the perioperative symptom assessment for lung surgery (PSA-Lung), to evaluate the recovery from symptoms and the functional status of patients undergoing uniportal VATS. We divided patients into two groups, according to patients' symptom scores, and compared the clinical characteristics between the two groups under each item. Then, we conducted a qualitative interview regarding coughing in postoperative week 4. Exactly 104 patients were enrolled in this study. The two highest-scoring patient-reported outcome (PRO) items were "shortness of breath" and "coughing" in the fourth week after surgery. Thirty-one patients reported that "coughing" severely influenced their lives in postoperative week 4. Using the PSA-Lung inventory, we found that "shortness of breath" was the worst symptom in postoperative week 4. Although "coughing" was not the most important symptom in the early postoperative period, it affected some patients' lives in postoperative week 4. Therefore, further research is required to determine the optimal cut-off point for coughing.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".