Italian validation of the Neck Dissection Impairment Index questionnaire
Bibliographic record
Abstract
Objective: The Neck Dissection Impairment Index (NDII) questionnaire is a useful and validated Quality of Life (QoL) evaluation instrument in patients undergoing major head-neck surgery. Its English version has been used in several studies in the last years. The aim of this work is to validate the NDII in Italian for both patient assessment and future studies. Materials and methods: Cross-cultural adaptation of the NDII was performed using standard techniques. Items of the original NDII were translated into Italian by a professional translator and two bilingual investigators. A final consensus version was obtained and given to two professional translators to produce a literal translation into English. The two translators and an expert committee synthesised the results of the translations in an English back-translated version that was compared with the original to check that they had the same semantic value. Results: Finally, a total of 42 patients completed both copies of the translated questionnaires. Internal consistency proved to be excellent, with Cronbach's alpha = 0.95. Conclusions: The NDII was successfully translated into Italian and its use was easy for patients. The translation of the NDII can represent a useful tool for individual patient assessment and future research.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".