The Multiligament Quality of Life Questionnaire
Bibliographic record
Abstract
BACKGROUND: Existing knee joint-specific outcome questionnaires lack content pertinent to patients with multiligament knee injuries. PURPOSE: To develop and test the reliability and validity of a novel disease-specific quality of life questionnaire for patients with multiligament knee injuries. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: Participants in this study included patients with multiligament knee injuries and clinician experts. Inclusion criteria were (1) patients with multiligament knee injury (age, 18-60 years), (2) ≥6 months after injury, and (3) operative or nonoperative treatment. Exclusion criteria were (1) preexisting osteoarthritis or inflammatory arthritis and (2) intracerebral/spinal cord injury. In phase I of the study, 85 eligible patients were mailed a questionnaire composed of 132 items from 11 existing knee questionnaires. Items were rated with regard to importance and frequency on a 5-point Likert scale. Criteria for inclusion in the first draft of the Multiligament Quality of Life (MLQOL) questionnaire included mean importance rating ≥3.5 and frequency <30% for the response "never experienced." In phase II, patient focus groups and expert interviews were conducted until no further new content was generated for the MLQOL, and in phase III, 99 eligible patients across 2 centers were mailed a preliminary MLQOL questionnaire along with the Tegner activity scale, Short Form (SF)-36, and anchor questions. Interitem and item-to-total correlations were used to perform item reduction to generate a final MLQOL instrument, which was tested for internal consistency (Cronbach alpha), test-retest reliability (interclass correlation coefficient [ICC]), and construct validity. RESULTS: At the end of phase III, a final MLQOL instrument was developed that was composed of 4 domains (physical impairments [PI], emotional impairments [EI], activity limitations [AL], and societal involvement [SI]) with 52 items in total. The MLQOL had adequate content validity, as none of the domains had any floor or ceiling effects. The Cronbach alpha was .94 (PI), .93 (EI), .94 (AL), and .91 (SI); ICC values were .89 (PI), .86 (EI), .91 (AL), and .88 (SI). Seven of 8 a priori hypotheses were satisfied, indicating good construct validity. CONCLUSION: The MLQOL instrument is a novel disease-specific quality of life tool that has demonstrated excellent content validity, reliability, and construct validity.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| 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".