Bibliographic record
Abstract
Background: The anterior approach (AA) technique has been advocated recently for right hepatectomy. However, the indications to opt for AA or conventional approach (CA) remain inconsistent. Objective: To evaluate preoperative factors influencing the approach for hepatectomy. Methods: A prospective study was performed on 17 patients who underwent hepatic resection from January 2014 to December 2015. All patients were planned to undergo hepatectomy with CA. The decision to adopt an AA was determined by the operating surgeon at the time of laparotomy when mobilization of the tumor before parenchymal transection was considered dangerous or difficult. Results: Comparing the pre operative characteristics of AA group with CA, there was no significant difference except for the total liver volume (TLV) (p = 0.0001), Tumor volume (TV) (p = 0.0001), and Largest Tumor Dimension (LTD) (p = 0.0001). Using Receiver Operating Characteristic Curve the volume with optimal sensitivity and specificity which may alter the intra-operative plan from conventional to anterior approach was at 1858 cc, 1130 cc and 11 cm for TLV, TV and LTD respectively. Outcome of hepatectomy in both groups were comparable to each other and to the available data. Conclusion: Of all the analyzed preoperative factors which may affect the approach for hepatectomy TLV, TV and LTD appear to be significant determinant factors.
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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".