Hand-assisted laparoscopic splenectomy versus open splenectomy for massive splenomegaly: 20-year experience at a Canadian centre
Bibliographic record
Abstract
BACKGROUND: Multiple techniques for splenectomy are now employed and include open, laparoscopic and hand-assisted laparoscopic splenectomy (HALS). Concerns regarding a purely laparoscopic splenectomy for massive splenomegaly (> 20 cm) arise from potentially longer operative times, higher conversion rates and increased blood loss. The HALS technique offers the potential advantages of laparoscopy, with the added safety of having the surgeon's hand in the abdomen during the operation. In this study, we compared the HALS technique to standard open splenectomy for the management of massive splenomegaly. METHODS: We reviewed all splenectomies performed at 5 hospitals in the greater Vancouver area between 1988 and 2007 for multiple demographic and outcome measures. Open splenectomies were compared with HALS procedures for spleens larger than 20 cm. Splenectomy reports without data on spleen size were excluded from the analysis. We performed Student t tests and Pearson χ(2) statistical analyses. RESULTS: A total of 217 splenectomies were analyzed. Of these, 39 splenectomies were performed for spleens larger than 20 cm. We compared the open splenectomy group (19 patients) with the HALS group (20 patients). There was a 5% conversion rate in the HALS group. Estimated blood loss (375 mL v. 935 mL, p = 0.08) and the mean (and standard deviation [SD]) transfusion rates (0.0 [SD 0.0] units v. 0.8 [SD 1.7] units, p = 0.06) were lower in the HALS group. Length of stay in hospital was significantly shorter in the HALS group (4.2 v. 8.9 d, p = 0.001). Complication rates were similar in both groups. CONCLUSION: Hand-assisted laparoscopic splenectomy is a safe and effective technique for the management of spleens larger than 20 cm. The technique results in shorter hospital stays, and it is a good alternative to open splenectomy when treating patients with massive splenomegaly.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".