Staplers or clips?
Bibliographic record
Abstract
BACKGROUND: Controlling of the renal vessels is a critical step in live donor nephrectomy (LDN). Currently, mainly 2 devices, Hem-o-Lok clips and staplers, are utilized to control vessels during LDN. Both of them have advantages and disadvantages. METHODS: This systematic review and meta-analysis was aimed to compare the safety and the efficacy of the 2 devices and to identify the better one in LDN. A systematic search for related publications in the databases of PubMed, Medline, Embase, the Cochrane Library, and Web of Science through February 2018 was performed. Eight studies were selected and evaluated with the Newcastle-Ottawa Scale (NOS). RESULTS: The meta-analysis result showed that utilization of Hem-o-Lok clips resulted in greater amount of estimated blood loss (mean differences [MD]: 40.10; 95% confidence interval [CI] 4.37-75.84) and longer time of warm ischemia (WIT) (MD: 55.61; 95% CI 36.79-74.43) than using staplers. Residual vascular length of grafts in clip group was longer than that in stapler group (MD: 2.51; 95% CI 0.78-4.24). However, the differences in primary outcomes such as device failure rate, death rate, and severe hemorrhage rate, were not significant between these 2 groups. In addition, utilization of Hem-o-Lok clips costed approximate $400 lower than staplers per patient. CONCLUSION: This study revealed that Hem-o-Lok clips and staplers have the similar function in LDN renal ligation, regarding the device failure rate, death rate, and severe hemorrhage rate. However, the surgeons would benefit from the clips in terms of the residue length of vessels, these outstanding features provide operation convenience and flexibility, such as right-sided donor nephrectomies, early vascular bifurcation, and rare vascular variation. In addition, the clips have potential economic advantages. In some developing countries, it would reduce the healthcare expenditure.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".