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Record W1125817528 · doi:10.1007/s00134-015-4028-2

Recovery after critical illness in patients aged 80 years or older: a multi-center prospective observational cohort study

2015· article· en· W1125817528 on OpenAlexafffund
Daren K. Heyland, Allan Garland, Sean M. Bagshaw, Kenneth Rockwood, Henry T. Stelfox, Peter Dodek, Robert Fowler, Alexis F. Turgeon, Karen E. A. Burns, John Muscedere, Martin Albert, Sangeeta Mehta, Xuran Jiang, Andrew G. Day

Bibliographic record

VenueIntensive Care Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsHôpital du Sacré-Cœur de MontréalKingston Health Sciences CentreSt. Michael's HospitalSunnybrook HospitalMount Sinai HospitalKingston General HospitalUniversity of CalgaryDalhousie UniversitySt. Paul's HospitalMcMaster UniversityCentre hospitalier universitaire de QuébecClinical Evaluation Research UnitUniversity of ManitobaManitoba HealthUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineObservational studyAnesthesiologyCritical illnessProspective cohort studyPain medicineCohort studyCenter (category theory)Emergency medicinePediatricsIntensive care medicineCritically illInternal medicineAnesthesia

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)0.001
metaresearch head score (Gemma)0.003
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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
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 head0.037
GPT teacher head0.328
Teacher spread0.291 · 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

Citations278
Published2015
Admission routes2
Has abstractno

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