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Record W3081996466 · doi:10.1097/md.0000000000022013

Prevalence and risk factors for intensive care unit acquired weakness

2020· article· en· W3081996466 on OpenAlexaboutno aff
Zheng Li, Qian Zhang, Peng Zhang, Ruixiang Sun, Haijiao Jiang, Jingjing Wan, Fang Wu, Xiaoye Wang, Xiubin Tao

Bibliographic record

VenueMedicine · 2020
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntensive care unitMEDLINECochrane LibraryIntensive care medicineWeaknessStatisticIntensive careCritically illMeta-analysisInternal medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Intensive care unit-acquired weakness (ICU-AW) is an acquired neuromuscular lesion and a common occurrence in patients who are critically ill. We will systematically summarize and incorporate the important risk factors and prevalence from previously published multivariate analyses for ICU-AW. METHODS: We will search the PubMed, Embase, Web of Science, and the Cochrane library to identify the relevant studies about the prevalence and risk factors for ICU-AW. Two reviewers will independently review the studies for eligibility according to the inclusion criteria. Two reviewers will independently assess the quality of studies by using the Newcastle-Ottawa scale for nonrandomized studies. Heterogeneity among studies will be estimated by the I statistic. RESULTS: This systematic review and meta-analysis will provide an evidence of prevalence and risk factors for the ICU-AW. CONCLUSION: We hope that our research will contribute to clinicians and public decision making about the ICU-AW.

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)0.042
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0070.006
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.039
GPT teacher head0.305
Teacher spread0.266 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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