Cross-cultural adaptation of the locally recurrent rectal cancer – Quality of life questionnaire
Bibliographic record
Abstract
AIM: The Locally Recurrent Rectal Cancer - Quality of Life (LRRC-QoL) questionnaire was developed as a disease specific measure of health-related quality of life (HrQoL) in locally recurrent rectal cancer (LRRC), it has previously been validated for use in the UK and Australia. The aim of this study was to translate and cross-culturally adapt the LRRC-QoL to enable its use on an international platform. MATERIALS AND METHODS: Cross-cultural adaptation of the LRRC-QoL was undertaken through a process of 1) Translatability Assessment (TA), 2) forward-backward translation, and 3) pre-testing interviews to establish content validity and conceptual equivalence across all versions. The QQ-10 measure was used to assess face validity and acceptability. The LRRC-QoL was translated into 13 languages: Danish, Dutch, French, Hindi, Italian, Mandarin, Marathi, Portuguese, Russian, Spanish, Swedish, Telugu, and Urdu. RESULTS: In total, 67 patients and 6 clinicians were recruited to pre-testing interviews across 12 countries: Brazil, Canada, Denmark, France, India, Italy, the Netherlands, New Zealand, Pakistan, Singapore, Spain, and Sweden. TA was also undertaken in the USA and Ireland, and translations were prepared in Russian, Marathi, and Telugu. The LRRC-QoL was found to demonstrate conceptual equivalence and content validity across all versions. Mean QQ-10 Value score 76.80 (SD 13.88) and mean Burden score 20.22 (SD 23.03), confirming face validity and acceptability in this international cohort. CONCLUSION: The LRRC-QoL has now undergone cross-cultural adaptation to enable its use in 10 languages and 16 countries. Its psychometric properties will be further examined through external validation in an international cohort.
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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 | 0.004 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".