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Record W2803293473 · doi:10.1093/ndt/gfy104.sp707

SP707CROSS-SECTIONAL VALIDITY OF EDMONTON SYMPTOM ASSESSMENT SYSTEM IN KIDNEY TRANSPLANT RECIPIENTS

2018· article· en· W2803293473 on OpenAlexaboutno aff
Yuri Battaglia, Giulia Piazza, Elena Martino, Sara Massarenti, Pasquale Esposito, Luana Peron, Alda Storari, Luigi Grassi

Bibliographic record

VenueNephrology Dialysis Transplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKidney transplantCross-sectional studyKidney transplantationInternal medicineIntensive care medicineKidneyPathology

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.019
GPT teacher head0.297
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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