MétaCan
Menu
Back to cohort
Record W2083122694 · doi:10.1089/neu.2013.2896

Toward an International Initiative for Traumatic Brain Injury Research

2013· article· en· W2083122694 on OpenAlexaboutno aff
Patrizia Tosetti, Ramona Hicks, Elizabeth Theriault, Anthony G. Phillips, Walter J. Koroshetz, Ruxandra Draghia‐Akli

Bibliographic record

VenueJournal of Neurotrauma · 2013
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsTraumatic brain injuryPsychologyMedicineNeurosciencePsychiatry

Abstract

fetched live from OpenAlex

The European Commission (EC) and the National Institutes of Health (NIH) jointly sponsored a workshop on October 18-20, 2011 in Brussels to discuss the feasibility and benefits of an international collaboration in the field of traumatic brain injury (TBI) research. The workshop brought together scientists, clinicians, patients, and industry representatives from around the globe as well as funding agencies from the EU, Spain, the United States, and Canada. Sessions tackled both the possible goals and governance of a future initiative and the scientific questions that would most benefit from an integrated international effort: how to optimize data collection and sharing; injury classification; outcome measures; clinical study design; and statistical analysis. There was a clear consensus that increased dialogue and coordination of research at an international level would be beneficial for advancing TBI research, treatment, and care. To this end, the EC, the NIH, and the Canadian Institutes of Health Research expressed interest in developing a framework for an international initiative for TBI Research (InTBIR). The workshop participants recommended that InTBIR initially focus on collecting, standardizing, and sharing clinical TBI data for comparative effectiveness research, which will ultimately result in better management and treatments for TBI.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.333
GPT teacher head0.448
Teacher spread0.114 · 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.

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

Citations80
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of NeurotraumaSame topicTraumatic Brain Injury and Neurovascular DisturbancesFrench-language works237,207