Factor Analyses and Validity of the Transplant Evaluation Rating Scale (TERS) in a Large Sample of Lung Transplant Candidates
Bibliographic record
Abstract
OBJECTIVE: It is well known that the occurrence of mental disorders is more common in lung transplant candidates compared to the general population. After transplantation mental disorders may negatively affect quality of life, adherence to immunosuppressive medication, as well as overall survival. Therefore, the identification of patients at risk is of utmost importance and in Germany pre-transplant psychosocial evaluation of the patients is required. To ensure high quality and comparability of these assessments, the use of psychometrically sound instruments is recommended. We applied the Transplant Evaluation Rating Scale (TERS), a broadly used expert interview. Two research groups have detected a two-factor structure of the TERS in different transplant samples; however, with slightly different results. The present study investigated which of the models would fit best in our sample of lung transplant patients. Additionally, we assessed convergent and predictive validity of the best fitting model to evaluate its clinical usefulness. METHODS: Between 2016 and 2019, 390 lung transplant candidates were evaluated and included in the study. The median age was 53 years and 54% were male. TERS interviews were conducted by trained medical doctors and psychologists. The participants completed questionnaires assessing quality of life and levels of depression and anxiety. Transplant- and disease-specific variables (lung disease, listing date, oxygen use) were taken from the patient charts. Confirmatory factor analysis was used to test the two proposed TERS-models in the present sample. RESULTS: The two-factor structure of the TERS reported by Hoodin and Kalbfleisch fit our sample best, showing good psychometric properties. The factor "emotional sensitivity" was highly correlated with quality of life and measures of psychosocial health while the factor "defiance" correlated with obstructive lung disease and older age but not with quality of life. The two factors showed differential predictive validity with regard to time until listing and pulmonary-specific quality of life 1 year after transplantation. CONCLUSIONS: The two factors showed good psychometric properties, and differential convergent and predictive validity. However, further studies concentrating on the predictive value of the TERS and its factors regarding somatic outcomes (mortality, graft functioning) are required.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".