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Record W3192546440 · doi:10.1007/s00134-021-06479-y

Association between sepsis survivorship and long-term cardiovascular outcomes in adults: a systematic review and meta-analysis

2021· review· en· W3192546440 on OpenAlexafffund
Leah Kosyakovsky, Federico Angriman, Emma Katz, Neill K. J. Adhikari, Lucas C. Godoy, John C. Marshall, Bruno L. Ferreyro, Douglas S. Lee, Robert S. Rosenson, Naveed Sattar, Subodh Verma, Augustin Toma, Marina Englesakis, Barry Burstein, Michael E. Farkouh, Margaret S. Herridge, Dennis T. Ko, Damon C. Scales, Michael E. Detsky, Lior Bibas, Patrick R. Lawler

Bibliographic record

VenueIntensive Care Medicine · 2021
Typereview
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsMontreal Heart InstituteTrillium Health CentreSunnybrook Health Science CentreSt. Michael's HospitalPublic Health OntarioTed Rogers Centre for Heart ResearchInstitute for Clinical Evaluative SciencesMcGill UniversitySinai Health SystemHealth Sciences CentreUniversity Health NetworkUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineSepsisHazard ratioMyocardial infarctionHeart failureStroke (engine)Internal medicineMeta-analysisIncidence (geometry)Cohort studyPopulationCochrane LibraryIntensive care medicineConfidence interval

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0140.028
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.168
GPT teacher head0.403
Teacher spread0.235 · 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.

Study designMeta-analysis
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

Citations80
Published2021
Admission routes2
Has abstractno

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