MétaCan
Menu
Back to cohort
Record W4392782916 · doi:10.7759/cureus.54376

Experience of Sustained Low-Efficiency Dialysis (SLED) in an Intensive Care Unit of a Quaternary Care Hospital

2024· article· en· W4392782916 on OpenAlexaff
Saleem Sharieff, Wajid Rafai, Adil Manzoor, Asim Idrees, Burhan Ahmad, Madiha Ghulam, Muhammad Usman Shabbir

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsGrand River Hospital
Fundersnot available
KeywordsMedicineRenal replacement therapyAcute kidney injuryDialysisIntensive care unitDemographicsSeptic shockRetrospective cohort studyCritically illIntensive care medicineCohortEmergency medicineSepsisInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In critically ill patients, sustained low-efficiency dialysis (SLED) has become a viable option for treating acute kidney injury (AKI) instead of continuous renal replacement therapy (CRRT). This study aimed to evaluate clinical outcomes in critically ill patients receiving SLED. MATERIAL AND METHODS: In our ICU, we performed a retrospective cohort study on hemodynamically unstable patients requiring dialysis in the form of SLED. Demographics, clinical, and biochemical variables were analyzed. RESULTS: A total of 58 patients were enrolled in the study. The mean age was 48.58 ± 15 with a male-to-female ratio of 3:1. Higher APACHE II score, high international normalized ratio, thrombocytopenia, and septic shock were found to be poor prognostic markers, with an overall observed mortality of 56.9%. CONCLUSION: SLED can be considered as an alternative to CCRT for selected hemodynamically unstable patients requiring renal replacement therapy.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.364
Teacher spread0.340 · 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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCureusSame topicAcute Kidney Injury ResearchFrench-language works237,207