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Record W2074935456 · doi:10.1164/rccm.201001-0024ws

Beyond Mortality: Future Clinical Research in Acute Lung Injury

2010· article· en· W2074935456 on OpenAlexaff
Roger G. Spragg, Gordon R. Bernard, William Checkley, J. Randall Curtis, Ognjen Gajić, Gordon Guyatt, Jesse B. Hall, Elliott Israel, Manu Jain, Dale M. Needham, Adrienne G. Randolph, Gordon D. Rubenfeld, David Schoenfeld, Bruce Thompson, Lorraine B. Ware, Duncan Young, Andrea L. Harabin

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of TorontoMcMaster University
FundersNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

Mortality in National Heart, Lung and Blood Institute-sponsored clinical trials of treatments for acute lung injury (ALI) has decreased dramatically during the past two decades. As a consequence, design of such trials based on a mortality outcome requires ever-increasing numbers of patients. Recognizing that advances in clinical trial design might be applicable to these trials and might allow trials with fewer patients, the National Heart, Lung and Blood Institute convened a workshop of extramural experts from several disciplines. The workshop assessed the current state of clinical research addressing ALI, identified research needs, and recommended: (1) continued performance of trials evaluating treatments of patients with ALI; (2) development of strategies to perform ALI prevention trials; (3) observational studies of patients without ALI undergoing prolonged mechanical ventilation; and (4) development of a standardized format for reporting methods, endpoints, and results of ALI trials.

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)0.197
metaresearch head score (Gemma)0.194
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.197
Threshold uncertainty score0.990

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1970.194
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0060.009
Science and technology studies0.0030.014
Scholarly communication0.0110.026
Open science0.0050.006
Research integrity0.0140.016
Insufficient payload (model declined to judge)0.0110.002

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 head0.095
GPT teacher head0.487
Teacher spread0.393 · 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.

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

Citations297
Published2010
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Respiratory and Critical Care MedicineSame topicClimate Change and Health ImpactsFrench-language works237,207