Prevalence and anatomical characteristics of the left medial basal pulmonary segment: a retrospective cohort study using three-dimensional computed tomography reconstruction
Bibliographic record
Abstract
OBJECTIVES: The left medial basal pulmonary segment (S7) has been largely overlooked in surgical literature due to the common belief that it is typically absent. This study aimed to determine the prevalence of left S7, identify its anatomical characteristics and evaluate outcomes in patients undergoing S7 segmentectomy. METHODS: We retrospectively analysed 1440 patients who underwent thoracoscopic segmentectomy for ground-glass opacity in the left lower lobe between January 2019 and February 2022 at our hospital. Three-dimensional computed tomography bronchography and angiography (3D-CTBA) images were reconstructed for all patients. The principal outcome was the prevalence and anatomical variation of S7. Secondary outcomes included surgical feasibility and short-term outcomes of S7 segmentectomy. RESULTS: Six types of left medial basal bronchus (B7) were identified. Type 1: B7 arose from B8 (61.4%); type 2: B7 arose higher than B8-B10 (6.3%); type 3: B7 arose from B9 (5.5%); type 4: B7 arose from both B8 and B9 (1.6%); type 5: B7 arose from both B8 and B10 (0.8%); type 6: B7 was absent (24.4%). Nine (0.6%) patients with nodules in S7 underwent successful thoracoscopic segmentectomy, with no major complications or conversions to lobectomy. CONCLUSIONS: Left S7 is present in approximately 75% of patients. The complex branching patterns of B7 identified highlight the importance of preoperative 3D-CTBA for accurate surgical planning. Our findings suggest that left S7 segmentectomy is feasible and safe when performed with precise anatomical understanding, expanding surgical options for patients with early-stage lung cancer in this segment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| 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".