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Record W3014371212 · doi:10.3390/jcm9040995

Screening Performance of Edmonton Symptom Assessment System in Kidney Transplant Recipients

2020· article· en· W3014371212 on OpenAlexaboutno aff
Yuri Battaglia, Luigi Zerbinati, Giulia Piazza, Elena Martino, Michele Provenzano, Pasquale Esposito, Sara Massarenti, Michele Andreucci, Alda Storari, Luigi Grassi

Bibliographic record

VenueJournal of Clinical Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychosocialChecklistMedical diagnosisPopulationComorbidityDistressCohortKidney transplantPhysical therapyPsychiatryClinical psychologyKidney transplantationInternal medicineTransplantationPathology

Abstract

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An average prevalence of 35% for psychiatric comorbidity has been reported in kidney transplant recipients (KTRs) and an even higher prevalence of other psychosocial syndromes, as defined by the Diagnostic Criteria for Psychosomatic Research (DCPR), has also been found in this population. Consequently, an easy, simple, rapid psychiatric tool is needed to measure physical and psychological symptoms of distress in KTRs. Recently, the Edmonton Symptom Assessment System (ESAS), a pragmatic patient-centred symptom assessment tool, was validated in a single cohort of KTRs. The aims of this study were: to test the screening performances of ESAS for the International Classification of Diseases-10th Revision (ICD-10) psychiatric diagnoses in KTRs; to investigate the optimal cut-off points for ESAS physical, psychological and global subscales in detecting ICD-10 psychiatric diagnoses; and to compare ESAS scores among KTR with ICD-10 diagnosis and DCPR diagnosis. 134 KTRs were evaluated and administered the MINI International Neuropsychiatric Interview 6.0 and the DCPR Interview. The ESAS and Canadian Problem Checklist (CPC) were given as self-report instruments to be filled in and were used to examine the severity of physical and psychological symptoms and daily-life problems. The physical distress sub-score (ESAS-PHYS), psychological distress sub-score (ESAS-PSY) and global distress score (ESAS-TOT) were obtained by summing up scores of six physical symptoms, four psychological symptoms and all single ESAS symptoms, respectively. Routine biochemistry, immunosuppressive agents, socio-demographic and clinical data were collected. Receiving Operating Characteristic (ROC) analysis was used to examine the ability of the ESAS emotional distress (DT) item, ESAS-TOT, ESAS-PSY and ESAS-PHYS, to detect psychiatric cases defined by using MINI6.0. The area under the ROC curve for ESAS-TOT, ESAS-PHYS, ESAS-PSY and DT item were 0.85, 0.73, 0.89, and 0.77, respectively. The DT item, ESAS-TOT and ESAS-PSY optimal cut-off points were ≥4 (sensitivity 0.74, specificity 0.73), ≥20 (sensitivity 0.85, specificity 0.74) and ≥12 (sensitivity 0.85, specificity 0.80), respectively. No valid ESAS-PHYS cut-off was found (sensitivity <0.7, specificity <0.7). Thirty-nine (84.8%) KTRs with ICD-10 diagnosis did exceed both ESAS-TOT and ESAS-PSY cut-offs. Higher scores on the ESAS symptoms (except shortness of breath and lack of appetite) and on the CPC problems were found for ICD-10 cases and DCRP cases than for ICD-10 no-cases and DCPR no-cases. This study shows that ESAS had an optimal screening performance (84.8%) to identify ICD-10 psychiatric diagnosis, evaluated with MINI; furthermore, ESAS-TOT and ESAS-PSY cut-off points could provide a guide for clinical symptom management 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)0.002
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.397
Teacher spread0.325 · 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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Citations14
Published2020
Admission routes1
Has abstractyes

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