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Record W2618418166 · doi:10.1093/ndt/gfx143.sp210

SP210THE IMPACT OF OBESITY ON RECOVERY FROM ACUTE KIDNEY INJURY: FEASIBILITY STUDY

2017· article· en· W2618418166 on OpenAlexaff
Rochelle Blacklock, Kelly Wright, Gerda K. Pot, Satish Jayawardene, Chris McIntyre, Iain C. Macdougall, Helen MacLaughlin

Bibliographic record

VenueNephrology Dialysis Transplantation · 2017
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineAcute kidney injuryObesityIntensive care medicineKidney diseaseKidneyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION AND AIMS: Acute kidney injury (AKI) is a rapid deterioration in kidney filtration rate or urine output, occurring in over 5% of hospital admission in the UK. AKI is associated with an increased risk of developing chronic kidney disease (CKD), resulting in an irreversible, and often progressive decline in kidney function. Obesity is also a risk factor for the development and worsening of CKD; however, the effect on recovery of kidney function after AKI, and the combined risk of obesity and AKI on subsequent development of CKD is not known. METHODS: A feasibility study was conducted to determine the feasibility of recruitment, retention and data collection procedures for the planned Ob-AKI cohort study in a sample of 100 patients hospitalised with an episode of AKI. Feasibility outcomes were monthly screening and recruitment uptake (more than 15% meeting inclusion criteria recruited), retention at 6 and 12 months (at least 80%), and completeness of data collection. Potential participants were identified by referral and electronic detection of episodes of AKI during their hospital admission, and recruited and consented whilst an inpatient or after discharge. Inclusion criteria were 18-85 years, episode of AKI (KDIGO 2012), and pre AKI creatinine measured within the previous 12 months. RESULTS: Screening and recruitment for a target of 100 patients was completed in 12 months in a single study centre; taking 6 months longer than planned. Electronic identification of AKI was not available for the first 9 months of recruitment. On average, 54 patients were screened each month, 27 added to the recruitment log, 19 were actually eligible and 7.5 consented to participate. 41% of eligible patients consented to participate in the study, far exceeding the feasibility target of 15%. 101 patients were recruited to the study (67M, 34F, mean age 63.5 (±12.6) years and mean BMI 29.9kg/m2 range 18.1 to 54.3kg/m2). 99 patients (98%) attended the baseline study visit (28.3% with stage I AKI, 21.2% stage 2 and 50.5% stage 3). At the baseline study visit, data were 100% complete for height, weight, blood pressure, reason for admission, diabetes and hypertension status, blood sampling and AKI staging including identifying the likely cause and classification as pre-renal renal or post-renal. Urine sampling was 96% complete and waist circumference was measured in 90% of patients. At 6 months, 85% (86/101) of patients remained in the study (7 withdrew, 4 died, 1 too unwell, 2 commenced HD), meeting the feasibility target for retention. CONCLUSIONS: Feasibility for recruitment and retention to a study addressing outcomes jointly associated with BMI and AKI has been demonstrated within a single centre, with a recruitment rate of 41% of eligible patients agreeing to participate in the study, and a high level of data completeness.

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)0.009
metaresearch head score (Gemma)0.010
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 score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.030
GPT teacher head0.372
Teacher spread0.343 · 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
Published2017
Admission routes1
Has abstractyes

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