Įvairių chirurginių metodų efektyvumo įvertinimas seilių liaukų navikų gydyme: sisteminė literatūros apžvalga
Bibliographic record
Abstract
Relevance and aim of the work: The purpose and aim of the study is to evaluate the effectiveness of different surgical techniques in the treatment of salivary gland tumours and identify the most effective surgical technique based on complications. Material and Methods:Design of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines by the PubMed database by using keywords such as “salivary gland tumours”, “surgery treatment of salivary gland tumours”, and “management of malignant salivary gland tumours”. The Newcastle-Ottawa Quality Assessment Scale – Cohort Studies was used to evaluate the quality of study and risk of bias. Results: Out of the 4,909 articles, 200 were duplicates. After excluding non-English publications (n=4,359), irrelevant studies, and overlapping data (n=332), six studies meet the inclusion criteria: five retrospective and one prospective study. Extracapsular dissection appears to be the most effective surgical technique compared to total excision or partial parotidectomy, particularly in terms of lower complication rates, decreased incidence of sialocele, and better preservation of nerve function and gland. Conclusions: This review demonstrates that extracapsular dissection (ECD) may offer better clinical outcomes for benign parotid tumours, especially pleomorphic adenomas, with improved functional results and fewer complications. The lack of statistically significant findings limits definitive conclusions. There is a need for further large-scale studies to support the clinical decision-making and confirm these trends. Keywords: Salivary gland tumours, Surgical techniques, Extracapsular dissection, Facial nerve.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".