Needs evaluation questionnaire for liver disease: a novel assessment of unmet needs in patients with chronic liver disease
Bibliographic record
Abstract
Patients with chronic liver disease face debilitating complications in their daily living and constantly report several types of unmet needs, but there is a paucity of validated questionnaires to assess these needs. In this study, we present the development of the Needs Evaluation Questionnaire for Liver Diseases (NEQ-LD) for the assessment of unmet needs in patients with chronic liver disease. Two hundred eighty-six outpatients with chronic liver diseases from a single tertiary referral center completed the NEQ-LD and related validity measures. Item response theory analyses were performed and demonstrated the strong psychometric properties of the questionnaire. Differential item functioning analyses showed that the scale functions equally across groups differing for age, sex, and presence of cirrhosis, suggesting the large applicability of the NEQ-LD for the assessment of unmet needs and between-group comparisons. Criterion validity measures provided evidence that unmet needs were positively associated with measures of depression and anxiety and negatively associated with measures of subjective well-being and physical and mental health. Unmet needs were expressed by a high percentage of patients, especially in the areas of information and dialogue with clinicians. One third of the sample reported material needs. Most of the items describing unmet needs were reported more frequently by patients with cirrhosis. Conclusion: We developed a reliable, valid, and largely employable instrument that can promote patient-centered care and facilitate support services in Hepatology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".