MétaCan
Menu
Back to cohort
Record W4280583799 · doi:10.1093/ehjacc/zuac054

Targeted temperature management after out of hospital cardiac arrest: quo vadis?

2022· article· en· W4280583799 on OpenAlexaff
Konstantin A. Krychtiuk, Christopher B. Fordyce, Carolina Malta Hansen, Christian Hassager, Jacob C. Jentzer, Venu Menon, Sarah M. Perman, Sean van Diepen, Christopher B. Granger

Bibliographic record

VenueEuropean Heart Journal Acute Cardiovascular Care · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsCanadian VIGOUR CentreUniversity of AlbertaUniversity of British Columbia
FundersLundbeckfondenÖsterreichischen Akademie der WissenschaftenMax Kade Foundation
KeywordsMedicineTargeted temperature managementIntensive care medicineMEDLINEEmergency medicineInternal medicineCardiopulmonary resuscitationResuscitationReturn of spontaneous circulation

Abstract

fetched live from OpenAlex

Targeted temperature management (TTM) has become a cornerstone in the treatment of comatose post-cardiac arrest patients over the last two decades. Belief in the efficacy of this intervention for improving neurologically intact survival was based on two trials from 2002, one truly randomized-controlled and one small quasi-randomized trial, without clear confirmation of that finding. Subsequent large randomized trials reported no difference in outcomes between TTM at 33 vs. 36°C and no benefit of TTM at 33°C as compared with fever control alone. Given that these results may help shape post-cardiac arrest patient care, we sought to review the history and rationale as well as trial evidence for TTM, critically review the TTM2 trial, and highlight gaps in knowledge and research needs for the future. Finally, we provide contemporary guidance for the use of TTM in daily clinical practice.

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)0.024
metaresearch head score (Gemma)0.110
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: Commentary · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.110
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0050.007
Open science0.0020.002
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.007
GPT teacher head0.230
Teacher spread0.223 · 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
GenreCommentary

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

Citations9
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Heart Journal Acute Cardiovascular CareSame topicCardiac Arrest and ResuscitationFrench-language works237,207