SP707CROSS-SECTIONAL VALIDITY OF EDMONTON SYMPTOM ASSESSMENT SYSTEM IN KIDNEY TRANSPLANT RECIPIENTS
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Improvements have been observed in long term survival of KTRs over the last two decades so HRQL has become an important measure of outcome. Although the most commonly used HRQL instruments in KTRs have items pertaining to physical and psychological symptoms, they do not directly assess patient self-report of troublesome symptoms. Edmonton Symptom Assessment System(ESAS)is a pragmatic patient-centred symptom assessment tool used for measuring physical and psychological symptom distress, validated in dialysis patient. Aims of study are to test the screening capabilities of ESAS for ICD-10 psychiatric diagnoses evaluated with MINI International Neuropsychiatric Interview 6.0(MINI6.0) and investigate optimal cut-off points for ESAS single and global items for detecting ICD-10 psychiatric diagnoses in KTRs. METHODS: KTRs, followed up in a single nephrology Unit, were evaluated. Each patient was individually administered MINI6.0. ESAS was given as self-report instruments to be filled in and was used to examine the severity of physical and psychological symptoms on a 0 to 10 scale. Emotional distress item(DT) was added as optional tenth psychological symptom. Physical distress sub-score(ESAS-PHYS), psychological distress sub-score(ESAS-PSY) and global distress score(ESAS-TOT)were given by summing up scores of six physical symptoms, four psychological symptoms and all single ESAS symptoms, respectively. Routine biochemistry, socio-demographic and clinical data were collected. Receiving Operating Characteristic (ROC) analysis was used to examine the ability of ESAS single item, ESAS-TOT, ESAS-PSY and ESAS-PHYS, to detect psychiatric cases defined by using MINI6.0. Combined sensitivities and specificities were visualized in ROC curve. RESULTS: Data pertaining to 134 out of 143 consecutive outpatients were collected. 67.2% were men and the mean age was 54.6 years (SD = 12.0). Higher scores on the ESAS symptoms (except shortness of breath), ESAS-TOT, ESAS-PHYS and ESAS-PSY sub-scores were found among ICD-10 cases than ICD-10 no-cases(Table1). Area under the ROC curve for DT item, ESAS-TOT, ESAS-PHYS and ESAS-PSY was 0,77, 0.89, 0.73 and 0.85, respectively(Figure 1). DT, ESAS-TOT and ESAS-PSY optimal cut-off points were ≥ 3(sensitivity 0.74, specificity 0.73), ≥20(sensitivity 0.85, specificity 0.74)and ≥ 12(sensitivity 0.85, specificity 0.80),respectively. Only 4 patients with an ICD-10 diagnosis did not exceed ESAS-TOT and ESAS-PSY cut-offs. CONCLUSIONS: This study reported, for the first time, the optimal screening capabilities(80%)of ESAS to identify ICD-10 cases evaluated with MINI as well as ESAS-PSY, ESAS-TOT and DT item cut-off points to detect ICD-10 psychiatric diagnoses in KTRs.
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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 | 0.004 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".