Lymph Node Dissection Is a Risk Factor for Short-Term Cough after Pulmonary Resection
Bibliographic record
Abstract
Cough is a common complication after pulmonary resection. However, the factors associated with cough that develop after pulmonary resection are still controversial. In this study, we used the Simplified Cough Score (SCS) and the Leicester Cough Questionnaire (LCQ) score to investigate potential risk factors for postoperative cough. Between January 2017 and June 2021, we collected the clinical data of 517 patients, the SCS at three days after surgery and the LCQ at two weeks and six weeks after surgery. Then, univariate and multivariate analyses were used to identify the independent risk factors for postoperative cough. The clinical baseline data of the cough group and the non-cough group were similar. However, the cough group had longer operation time and more blood loss. The patients who underwent lobectomy were more likely to develop postoperative cough than the patients who underwent segmentectomy and wedge resection, while the patients who underwent systematic lymph node dissection were more likely to develop postoperative cough than the patients who underwent lymph node sampling and those who did not undergo lymph node resection. When the same lymph node management method was applied, there was no difference in the LCQ scores between the patients who underwent wedge resection, lobectomy and segmentectomy. The lymph node resection method was an independent risk factor for postoperative cough (p < 0.001). Conclusions: Lymph node resection is an independent risk factor for short-term cough after pulmonary resection with video-assisted thoracoscopic surgery, and damage to the vagus nerve and its branches (particularly the pulmonary branches) is a possible cause of short-term cough. The mechanism of postoperative cough remains to be further studied.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".