Assessing Social Difficulties in Patients Treated with Kidney Replacement Therapy (Dialysis or Kidney Transplant)
Bibliographic record
Abstract
Background: The Social Difficulties Inventory (SDI) is used in the clinical management of patients with cancer in the UK. We examine the construct validity of the SDI in patients with kidney replacement therapy (KRT: dialysis or kidney transplant [KT]). Methods: This is a secondary analysis of data collected in multicenter, cross-sectional studies. Adults receiving KRT completed the SDI and other patient reported outcome measures. Clinical and sociodemographic characteristics were also collected. For SDI, the degree of difficulty is rated: no difficulty, a little, quite a bit or very much. 16 items form the SD16 and three subscales: “Everyday Living”, “Money Matters” and “Self and Others.” We used Cronbach's alpha to assess reliability. We assessed the correlation of SD16 and its subscales with variables that measure similar constructs. Further, we compared scores between groups that are expected to have different degree of difficulties. Results: 788 participants (mean[SD] age 57[15] years) completed the SDI. 61% of them were male and 58% were on dialysis. Internal consistency was good for all scales: α=0.87, 0.82, 0.75, 0.88, for “Everyday Living”, “Money Matters”, “Self and Others” subscales and the SD16, respectively. The “Everyday Living” subscale was moderately correlated depression (Rho=0.61, p<0.001) and physical functioning (Rho=0.72, p<0.001). The Self and Other” subscale was moderately correlated with depression (Rho=0.56, p<0.001). SD16 scores were higher for patients on dialysis vs KT (median[interquartile range - IQR] 7[3,13] vs 3[1,8]p<0.001). “Everyday Living” scores were higher in patients with Charlson Comorbidity Index of ≥4 ([3[0,6.5] vs 1[0,3.5]p<0.001). “Money Matters” scores were higher in individuals facing high vs low material deprivation (1[0,4] vs 0[0,3] p<0.008). “Self and Other” scores were higher in participants that are uncomfortable or reluctant in relationships vs those that find it easy (3[1,7] vs 1[0,3]p<0.002). Conclusions: These results suggests that the SD-16 and its subscales have good reliability and structural validity. Further research is required to explore the potential clinical benefits of using the SD16 in patients with kidney failure.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".