The Italian Version of the Majeed Pelvic Score: Translation, Cross-Cultural Adaptation and Validation
Bibliographic record
Abstract
Abstract Purpose The assessment of functional outcomes after pelvic ring fracture remains a controversial topic. The Majeed pelvic score (MPS) is the most commonly used pelvic-specific questionnaire in the literature. The aim of this study is translation, cross-cultural adaptation and validation of the Italian version of MPS.Methods The study was articulated in two phases. Phase 1 consisted in translation and cross-cultural adaptation of MPS, from English into Italian. The psychometric properties were tested on 52 Italian patients (Phase 2). Construct validity was assessed by correlation with Short-Form 12 (SF-12). 33 patients repeated the questionnaire after 14 days to assess its reproducibility. All data were subsequently analyzed (descriptive statistics, multitrait analysis, reliability and construct validity assessment).Results The questionnaire was clear and easily understood (no missing data). A ceiling effect was detected for all items of the scale. Multitrait analysis showed good results for each outcome measure, except for the item “walking distance” that showed poor item discriminant validity. A significant correlation between the MPS and the physical component summary (PCS) of the SF-12 was found, while there was a weak correlation with the mental component summary (MCS). The questionnaire showed high internal consistency (Cronbach’s alpha: 0.91–0.99) and very good test-retest reliability (intraclass correlation coefficients: 0.92–0.96).Conclusions The Italian version of the MPS has demonstrated to be reliable and valid in the evaluation of patients with pelvic ring fractures. There is still however a need for an instrument capable of evaluating the mental component in these types of injuries.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".