Comparative Effectiveness of Minimally Invasive Surgery vs Open Surgery in neuroblastoma
Bibliographic record
Abstract
Abstract Purpose: To explore the feasibility of minimally invasive surgery（MIS）used in the treatment of neuroblastoma. Methods: Selected NB patients were randomly assigned to the MIS group and the open surgery（OS） group. The operative details, major complications, and prognosis of the two groups were compared. Results: A total of 35 children undergoing MIS and 35 children undergoing open surgery were enrolled in this study. According to the INRGSS staging system, 30 patients were classified as stage L1, 32 as stage L2, and 8 as stage M. No statistical difference was found in the age of the children and the maximum diameter of the primary site tumor between the two groups. The bleeding volume in the OS group was significantly higher than that in the MIS group (P=0.006), The time to start postoperative feeding in the MIS group was significantly shorter than that in the OS group (P<0.001). No significant difference was found in the number of GTR between the MIS group and the OS group (P=0.246). The one-year survival rate and overall survival rate of the MIS group were 100% and 93.85%, while the OS group was 100% and 93.72%, respectively. Conclusion: MIS has more advantages than OS for suitable neuroblastoma, while the prognosis is almost the same. After gradually improving the indications for MIS, it should become the preferred surgical method for children within this range.
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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 | 0.001 |
| 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".