2b or not 2b? Shoulder function after level 2b neck dissection: A double‐blind randomized controlled clinical trial
Bibliographic record
Abstract
BACKGROUND: Selective neck dissection (SND) is a mainstay of head and neck cancer treatment. A common sequela is shoulder syndrome from spinal accessory nerve (SAN) trauma. Extensive dissection in neck levels 2 and 5 leads to SAN dysfunction. However, it is not known whether limited level 2 dissection reduces SAN injury. The purpose of this double-blind randomized controlled trial was to determine whether omitting level 2b dissection would improve shoulder-related quality of life and function. METHODS: Patients with head and neck cancers undergoing surgery were randomized 1:1 to SND without level 2b dissection (group 1) or with it (group 2) on their dominant-hand side. Patients, caregivers, and assessors were blinded. The primary outcome was the change in the Neck Dissection Impairment Index (NDII) score after 6 months. An a priori calculation of the minimally important clinical difference in the NDII score was determined to establish a sample size of 15 patients per group (power = 0.8). Secondary outcomes included shoulder strength and range of motion (ROM) and SAN nerve conduction. The trial was registered at ClinicalTrials.gov (NCT00765791). RESULTS: Forty patients were enrolled, and 30 were included (15 per group). Six months after the surgery, group 2 demonstrated a significant median decrease in the NDII from the baseline (30 points) and in comparison with group 1, whose NDII dropped 17.5 points (P = .02). Shoulder ROM and SAN conduction demonstrated significant declines in group 2 (P ≤ .05). No adverse events occurred. CONCLUSIONS: Level 2b should be omitted in SND when this is oncologically safe and feasible. This allows for an optimal balance between function and cancer cure.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".