Comparison of Robot-Assisted and Conventional Neck Dissection in Head and Neck Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Main outcome(s) operative time, intraoperative blood loss, duration of drain placement, total drainage volume, total LN yield, positive LN yield, length of hospital stay (LOS), postoperative cosmetic satisfaction, regional LN recurrence rate, and incidence ofcomplications. Quality assessment / Risk of bias analysis Newcastle-Ottawa Scale.Strategy of data synthesis Data analysis was conducted using Comprehensive Meta-Analysis software (version 3, Biostat, Englewood, NJ, USA).Mean differences (MDs) were employed to compare parameters such as operative time, intraoperative blood loss, lymph node yields, drainage measures, length of stay (LOS), and postoperative cosmetic satisfaction between the RNDRM and CND groups.Risk differences (RDs) were used to assess nodal recurrence rates and postoperative complications.All analyses were based on a random-effects model. Subgroup analysis Subgroup analysis will be conducted if needed. Sensitivity analysis Leave-one-out sensitivity analysis.Country(ies) involved Taiwan.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".