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Record W2954634460 · doi:10.1007/s12028-019-00767-8

The Future of Neurocritical Care Research: Proceedings and Recommendations from the Fifth Neurocritical Care Research Network Conference

2019· article· en· W2954634460 on OpenAlexaff
Sara E. Hocker, Syed Omar Shah, Paul Vespa, J. Javier Provencio, Eusebia Calvillo, DaiWai M. Olson, Chethan P. Venkatasubba Rao, J. Claude Hemphill, Raimund Helbok, T. Human, Hooman Kamel, Lori Madden, Paul Nyquist, Oladi Bentho, K. O’Phelan, John J. Lewin, Sheila Alexander, Wendy Ziai, Sherry Chou, Fred Rincón, Molly McNett, Nerissa Ko, Brian J. Zink, Denise H. Rhoney, Michael N. Diringer, Robert D. Stevens, C S Robertson, Gustavo Sampaio, Lori Shutter, Geoffrey Ling, Muhammad Abdul Rehman, Sherif Hanafy Mahmoud, Susan Yeager, Sarah Livesay, José I. Suarez

Bibliographic record

VenueNeurocritical Care · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategies and Innovation
Canadian institutionsUniversity of Alberta
FundersNational Institute of Neurological Disorders and Stroke
KeywordsNeurointensive careMedicineJuryPain medicineMedical educationMedical emergencyIntensive care medicineAnesthesiologyPolitical science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.062
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.326

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.059
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0060.006
Science and technology studies0.0060.005
Scholarly communication0.0280.017
Open science0.0040.010
Research integrity0.0300.024
Insufficient payload (model declined to judge)0.0140.003

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 head0.075
GPT teacher head0.340
Teacher spread0.265 · 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 designNot applicable
DomainMethods
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

Citations12
Published2019
Admission routes1
Has abstractno

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