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Psychosocial Dimensions in Hemodialysis Patients on Kidney Transplant Waiting List: Preliminary Data

2020· article· en· W3111909594 on OpenAlexaboutno aff
Yuri Battaglia, Luigi Zerbinati, Elena Martino, Giulia Piazza, Sara Massarenti, Alda Storari, Luigi Grassi

Bibliographic record

VenueTransplantology · 2020
Typearticle
Languageen
FieldMedicine
TopicNeurological Complications and Syndromes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychosocialIrritabilityIntensive care medicinePsychiatryAnxiety

Abstract

fetched live from OpenAlex

Although the donation rate for deceased and living kidneys has been increasing, the donor organ availability meets only the 30% of kidney needs in Italy. Consequently, hemodialysis patients stay for a long time, an average of 3.2 years, on a waiting list for a kidney transplant with consequent relevant psychological distress or even full-fledged psychiatric disorders, as diagnosed with traditional psychiatric nosological systems. Recent studies report, however, a higher prevalence of other psychosocial syndromes, as diagnosed by using the Diagnostic Criteria for Psychosomatic Research (DCPR) in medically ill and kidney transplant patients. Nevertheless, no data regarding DCPR prevalence are available in patients waitlisted for a renal transplant (WKTs). Thus, the primary aim of this study was to identify sub-threshold or undetected syndromes by using the DCPR and, secondly, to analyze its relationship with physical and psychological symptoms and daily-life problems in WKTs. A total of 30 consecutive WKTs were assessed using the DCPR Interview and the MINI International Neuropsychiatric Interview 6.0. The Edmonton Symptom Assessment System (ESAS) and the Canadian Problem Checklist were used to assess physical and psychological distress symptoms and daily-life problems. A total of 60% of patients met the criteria for at least one DCPR diagnosis; of them, 20% received one DCPR diagnosis (DCPR = 1), and 40% more than one (DCPR > 1), especially the irritability cluster (46.7%), Abnormal Illness Behavior (AIB) cluster (23.3%) and somatization cluster (23.3%). Fifteen patients met the criteria for an ICD diagnosis. Among patients without an ICD-10 diagnosis, 77.8% had at least one DCPR syndrome (p < 0.05). Higher scores on ESAS symptoms (i.e., tiredness, nausea, depression, anxiety, feeling of a lack of well-being and distress), ESAS-Physical, ESAS-Psychological, and ESAS-Total were found among DCPR cases than DCPR non-cases. In conclusion, a high prevalence of DCPR diagnoses was found in WKTs, including those who resulted to be ICD-10 non-cases. The joint use of DCPR and other screening tools (e.g., ESAS) should be evaluated in future research as part of a correct psychosocial assessment of WKTs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.040
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

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

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 head0.078
GPT teacher head0.304
Teacher spread0.226 · 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 teacher head, 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".

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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