Patient Concerns Inventory for Arabic Patients with Head and Neck Cancer: A Cross-Cultural Adaptation and Preliminary Validation
Bibliographic record
Abstract
INTRODUCTION: Head and neck cancer (HNC) treatments often lead to significant post-treatment side effects that affect patients' quality of life. This study aimed to translate and validate the post-treatment Patient Concerns Inventory for head and neck (PCI-HN) into Arabic among HNC survivors. METHODS: This study employed a cross-sectional design, where PCI-HN was translated and assessed for content and face validity by clinical experts and patients, respectively. Revisions to multiple items related to 'social and religious welfare'. Patients' responses were then analysed to assess internal consistency (Cronbach's alpha) and test-retest reliability (Cohen's Kappa). RESULTS: Thirty-eight participants (19 males, 19 females, mean age 50.68 ± 16.13 years) were included. The Arabic PCI-HN demonstrated good overall internal consistency (α = 0.723) but fair test-retest agreement (κ = 0.22), likely reflecting dynamic changes in HNC post-treatment experiences. CONCLUSION: The Ar-PCI-HN can be a helpful instrument for capturing distinct aspects of the survivorship experience among Arabic-speaking HNC survivors. Determining the clinical interpretability and ability to detect changes over time requires further multi-centre and multi-country clinical studies. This would be necessary to ensure its integration into routine outpatient consultations for Arabic-speaking patients in Arab countries and globally.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".