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Record W3211989507 · doi:10.1155/2021/6481559

Lessons Learned from a Small Pediatric Continuous Renal Replacement Therapy Program

2021· article· en· W3211989507 on OpenAlexaff
Tanya Holt, Olivia Griffin, Amélie Cyr, Rebecca Brockman, Laura Wihak, Gregory Hansen

Bibliographic record

VenueCritical Care Research and Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRenal replacement therapyMedicineDosingIntensive care medicineCritically illEmergency medicineSepsisInternal medicine

Abstract

fetched live from OpenAlex

Continuous renal replacement therapy (CRRT) has become a pillar of care in pediatric intensive care units (PICUs) over the past few decades. Quality indicators (QIs) have been evaluated that reflect safe and accountable CRRT. However, there is a paucity of data on outcomes and QIs in smaller-volume CRRT programming. The purpose of this retrospective study was to evaluate the efficiencies, effectiveness, and outcomes of a small-volume CRRT program. Eighty-two patients received CRRT over a 13-year period, and 79% survived to discharge. Sepsis or nonseptic shock (n = 11 (22%) versus n = 6 (50%); <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>p</a:mi> </a:math> value = 0.004) and time to CRRT initiation after PICU admission (1.1 versus 5.0 days; <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>p</c:mi> </c:math> value = 0.005) were independent predictors for mortality. The program also had positive outcomes for QIs related to CRRT efficiency and time of initiation, dosing delivery, and rate of adverse events. This study is important as it illustrates the opportunity that smaller centers have to initiate CRRT programming and provide safe and effective care.

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)0.002
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.367
GPT teacher head0.551
Teacher spread0.184 · 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.

Study designNot applicable
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

Citations4
Published2021
Admission routes1
Has abstractyes

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