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Record W2326613362 · doi:10.1136/thoraxjnl-2016-208466

Reporting data on long-term follow-up of critical care trials

2016· editorial· en· W2326613362 on OpenAlexaff
May Hua, Hannah Wunsch

Bibliographic record

VenueThorax · 2016
Typeeditorial
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsSunnybrook Hospital
FundersNational Institute on Aging
KeywordsMedicineARDSIntensive care unitRosuvastatinSepsisIntensive care medicinePopulationRandomized controlled trialEmergency medicineInternal medicineLung

Abstract

fetched live from OpenAlex

Over the past 20 years, the lens of critical care research has widened, with 60 or 90 days becoming an increasingly common end point for observation of mortality and other outcomes.1–6 There have also been an increase in studies that follow patients for many years to understand how and when recovery occurs and whether risks attributable to critical illness may diminish.7–9 Correspondingly, when new therapies aimed at improving outcomes are studied, there is increasing recognition that reporting of short-term (ie, hospital) outcomes is not enough and that evaluation of the long-term effects of the intervention can be very important. Dinglas et al present long-term follow-up data of patients enrolled in the Statins in Acutely Injured Lungs in Sepsis (SAILS) trial, which examined the use of rosuvastatin in patients with sepsis-associated acute respiratory distress syndrome (ARDS).10 The SAILS trial randomised patients within 48 h of enrolment to receive either rosuvastatin or placebo for a maximum of 28 days (or until the third day after discharge from the intensive care unit (ICU), hospital discharge or death). The primary outcome of the original study was 60-day in-hospital mortality. As part of the planned analysis, the authors evaluated the long-term outcome of these patients to determine the safety of in-hospital statin use in survivors of sepsis-associated ARDS.11 The rationale was that there are known adverse effects of statins on skeletal muscle and psychological symptoms that can occur, and critically ill patients represent a population already at risk of physical and psychological difficulties. The authors collected detailed data on physical performance and psychological symptoms at 6 and …

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.689
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.689
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.125
GPT teacher head0.461
Teacher spread0.336 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations5
Published2016
Admission routes1
Has abstractyes

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