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Record W4317567785 · doi:10.1097/ccm.0000000000005721

Mortality As a Measure of Treatment Effect in Clinical Trials Recruiting Critically Ill Patients*

2023· review· en· W4317567785 on OpenAlexaff
Jan O. Friedrich, Michael O. Harhay, Derek C. Angus, Karen E. A. Burns, Dean Fergusson, Simon Finfer, Paul C. Hébert, Kathy Rowan, Gordon D. Rubenfeld, John C. Marshall

Bibliographic record

VenueCritical Care Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreCentre Hospitalier de l’Université de MontréalOttawa HospitalMcMaster UniversityUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineInterquartile rangeRandomized controlled trialRandomizationClinical trialMortality rateIntensive careMEDLINEPsychological interventionClinical endpointIntensive care medicinePediatricsEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: All-cause mortality is a common measure of treatment effect in ICU-based randomized clinical trials (RCTs). We sought to understand the performance characteristics of a mortality endpoint by evaluating its temporal course, responsiveness to differential treatment effects, and impact when used as an outcome measure in trials of acute illness. DATA SOURCES: We searched OVID Medline for RCTs published from 1990 to 2018. STUDY SELECTION: We reviewed RCTs that had randomized greater than or equal to 100 patients, were published in one of five high-impact general medical or eight critical care journals, and reported mortality at two or more distinct time points. We excluded trials recruiting pediatric or neonatal patients and cluster RCTs. DATA EXTRACTION: Mortality by randomization group was recorded from the article or estimated from survival curves. Trial impact was assessed by inclusion of results in clinical practice guidelines. DATA SYNTHESIS: From 2,592 potentially eligible trials, we included 343 RCTs (228,784 adult patients). While one third of all deaths by 180 days had occurred by day 7, the risk difference between study arms continued to increase until day 60 (p = 0.01) and possibly day 90 (p = 0.07) and remained stable thereafter. The number of deaths at ICU discharge approximated those at 28-30 days (95% [interquartile range [IQR], 86-106%]), and deaths at hospital discharge approximated those at 60 days (99% [IQR, 94-104%]). Only 13 of 43 interventions (30.2%) showing a mortality benefit have been adopted into widespread clinical practice. CONCLUSIONS: Our findings provide a conceptual framework for choosing a time horizon and interpreting mortality outcome in trials of acute illness. Differential mortality effects persist for 60 to 90 days following recruitment. Location-based measures approximate time-based measures for trials conducted outside the United States. The documentation of a mortality reduction has had a modest impact on 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)0.370
metaresearch head score (Gemma)0.649
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.630
Threshold uncertainty score0.777

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3700.649
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0120.017
Bibliometrics0.0130.017
Science and technology studies0.0010.004
Scholarly communication0.0080.007
Open science0.0030.003
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.870
GPT teacher head0.745
Teacher spread0.125 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
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

Citations18
Published2023
Admission routes1
Has abstractyes

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