Efficacy of segmental resection in patients with prenatally diagnosed congenital lung malformations
Bibliographic record
Abstract
Objectives: Lung segmental resection is a better treatment option than lobectomy for patients with prenatally diagnosed congenital lung malformations (CLMs). However, data are lacking on the effects of this procedure in prenatally diagnosed CLM patients. In this study, we explored whether parenchyma-saving resection was feasible in patients with this condition. Methods: A retrospective analysis was performed on 27 patients prenatally diagnosed with CLM, who subsequently underwent surgery between March 2011 and September 2015. Lobectomies and segmental resections were performed in 7 and 20 patients, respectively, based on the extent of cystic lesion invasion. Results: The operative time significantly differed between the two groups (lobectomy group, 92.9 ± 32.0 min; segmental resection group, 126.5 ± 37.5 min). However, the duration of chest tube drainage and the length of hospital stay did not significantly differ between the groups. Chest computed tomography (CT) was performed during follow-up on all but 3 patients. We encountered 2 cases of remnant lesions, and one instance of a small emphysematous lesion around the surgical site was noted in either group. Conclusions: Lung-sparing surgery is relatively safe with few complications. In this study, the incidence of remnant lung lesions (a drawback of segmentectomy) was low. Thus, segmental resection affords results similar to those of lobectomy in patients with prenatally diagnosed CLM. Furthermore, segmental resection can preserve lung volume, thereby maintaining later pulmonary function. Therefore, elective segmental resection performed after precise identification of the lesions' locations may be highly beneficial for CLM patients.
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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".