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Record W1628136731 · doi:10.1186/cc2964

Monitoring and oversight in critical care research.

2004· article· en· W1628136731 on OpenAlexaff
James V. Lavery, Marleen LP Van Laethem, Arthur S. Slutsky

Bibliographic record

VenueCritical Care · 2004
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of TorontoCentre for Global Health ResearchSt. Michael's Hospital
Fundersnot available
KeywordsMedicineResearch ethicsInformed consentClinical researchEngineering ethicsInstitutional review boardProtocol (science)Medical educationPublic relationsAlternative medicinePolitical sciencePathologyPsychiatry

Abstract

fetched live from OpenAlex

Institutionally based research ethics review is a form of peer review that has - for better or worse - become the norm throughout the world. The vast majority of research ethics review takes the form of protocol review alone, conducted in advance of the research. Although oversight and monitoring in clinical research have long been recognized as essential features of sound research ethics, they are seldom exercised in ways that fulfill their motivating goals: to ensure that research is conducted as planned; that research participants comprehend the information presented to them in the consent process; and that the potential benefits and risks of study participation remain acceptable. Annual review of continuing research, monitoring informed consent, monitoring adherence to approved protocols and monitoring integrity of research data comprise the main types of monitoring and oversight activity. We believe that our institutionally based systems of research ethics review and responsibility require greater engagement and participation from researchers and research administrators. The appropriate role of critical care researchers and research administrators is to provide leadership to move toward a greater recognition of the importance of monitoring and oversight for ethical and high quality clinical research.

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)0.001
metaresearch head score (Gemma)0.091
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.091
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.707
GPT teacher head0.700
Teacher spread0.007 · 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 designTheoretical or conceptual
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

Citations21
Published2004
Admission routes1
Has abstractyes

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