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Record W4293062210 · doi:10.5772/intechopen.105033

Renal Replacement Therapies in the Intensive Care Unit

2022· book-chapter· en· W4293062210 on OpenAlexaff
Dominic Godbout, Philippe Lachance, Jean Côté

Bibliographic record

VenueIntechOpen eBooks · 2022
Typebook-chapter
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversité de MontréalMcGill University Health CentreUniversité du Québec
Fundersnot available
KeywordsRenal replacement therapyIntensive care medicineMedicineIntensive care unitCritically illIntensive careTroubleshootingMedical prescriptionModalitiesDosingInternal medicineNursing

Abstract

fetched live from OpenAlex

Renal replacement therapies (RRT) are commonly used in critically ill patients to achieve solute clearance, maintain acid-base status, and remove fluid excess. The last two decades have seen the emergence of large randomized control trials bringing new evidence regarding how RRT should now be managed in the ICU. RRT is considered a vital supportive care and needs to be adequately prescribed and delivered. This chapter first summarizes the basic principles and characteristics of the three major RTT modalities: intermittent hemodialysis (IHD), prolonged intermittent RRT (PIRRT), and continuous RRT (CRRT). Then, the large body of literature regarding indications for initiation (early vs late), choice of modality (intermittent vs continuous and diffusion vs convection), dosing (intensive vs less-intensive), and anticoagulation alternatives is reviewed to guide clinical decision-making. Recent evidence in the optimal timing of discontinuing RRT is reported. Finally, troubleshooting scenarios frequently seen in clinics and requiring an adapted RRT prescription are also discussed.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0520.027

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 head0.071
GPT teacher head0.350
Teacher spread0.279 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2022
Admission routes1
Has abstractyes

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