Quantifying Neck Fibrosis: Establishing the Domain Structure of the Neck Fibrosis Scale
Bibliographic record
Abstract
OBJECTIVE: We recently described the development of the Neck Fibrosis Scale (NFS). In this submission, we confirm domain structure and validate a scoring system for the NFS. STUDY DESIGN: Prospective cross-sectional study. METHODS: Between January 2020 and December 2021, 127 head and neck cancer patients with varying degrees of cutaneous neck fibrosis completed the original 15 item NFS. Exploratory factor analysis was used to identify optimal groupings with similar underlying factors. The association between the domains of the NFS and various measures of neck morbidity (i.e., construct validity) were assessed using gamma regression. RESULTS: Exploratory factor analysis confirmed 13 of the 15 items from the NFS mapped onto two factors, which were labelled 'physical' and 'emotional' domains. Of the remaining two items, 'energy' did not load uniquely onto one factor and was removed. 'Neck-swelling' did not load on either factor (loadings <0.3) but was retained within the physical domain based on clinical importance. This resulted in a revised 14-item questionnaire. Internal consistency for these two domains was high (>0.8, p < 0.01). Both the physical and emotional domains of the revised NFS show strong correlation with the neck dissection impairment index and neck range of motion. The physical domain strongly correlated with neck elasticity (0.902 [95%CI 0.839-0.972], p < 0.01). Patients receiving multimodal therapy had physical domain scores that were 31.6% [95% 13.9-51.8] higher (worse) than unimodal therapy patients. CONCLUSIONS: A domain structure and scoring strategy have been developed for the NFS. Future efforts should be directed toward an evaluation of responsiveness. LEVEL OF EVIDENCE: NA Laryngoscope, 133:2198-2202, 2023.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".