Canadian French Translation and Validation of the Neck Dissection Impairment Index: A Quality of Life Measure for the Surgical Oncology Population
Bibliographic record
Abstract
BACKGROUND: Neck dissections (ND) are a routine procedure in head and neck oncology. Given the postoperative functional impact that some patients experience, it is imperative to identify and track quality of life (QoL) symptomatology to tailor each patient's therapeutic needs. To date, there is no validated French-Canadian questionnaire for this patient-population. We therefore sought to translate and validate the Neck Dissection Impairment Index (NDII) in Canadian French. METHODS: A 3-phased approach was used. Phase 1: The NDII was translated from English to Canadian French using a "forward and backward" translational technique following international guidelines. Phase 2: A cognitive debriefing session was held with 10 Canadian French-speaking otolaryngology patients to evaluate understandability and acceptability. Phase 3: The final version was administered prospectively to 30 patients with prior history of ND and 30 control patients. These patients were asked to complete the questionnaire 2 weeks after their first response. Test-retest reliability was calculated with Spearman's correlation. Internal consistency was elicited using Cronbach's alpha. RESULTS: NDII was successfully translated and validated to Canadian French. Cronbach's alpha revealed high internal consistency (0.92, lower 95% confidence limit 0.89). The correlation for test-retest validity were strong or very strong (0.61-0.91). CONCLUSION: NDII is an internationally recognized QoL tool for the identification of ND-related impairments. This validated Canadian French version will allow clinicians to adequately assess the surgery-related QoL effect of neck surgery in the French-speaking population, while allowing French institutions to conduct and/or participate in multisite clinical trials requiring the NDII as an outcome measure.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".