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Record W3015387786 · doi:10.17863/cam.59155

A management algorithm for patients with intracranial pressure monitoring: the Seattle International Severe Traumatic Brain Injury Consensus Conference (SIBICC).

2019· article· en· W3015387786 on OpenAlexaff
Gregory W. J. Hawryluk, Sergio Aguilera, András Büki, Eileen M. Bulger, Giuseppe Citerio, D. James Cooper, Ramon Diaz Arrastia, Michael N. Diringer, Anthony Figaji, Guoyi Gao, Romergryko G. Geocadin, Jamshid Ghajar, Odette A. Harris, Alan Hoffer, Peter J. Hutchinson, Mathew Joseph, Ryan S. Kitagawa, Geoffrey T. Manley, Stephan A. Mayer, David Menon, Geert Meyfroidt, Daniel B. Michael, Mauro Oddo, David O. Okonkwo, Mayur B. Patel, Claudia Robertson, Jeffrey V. Rosenfeld, Andrés M. Rubiano, Juan Sahuquillo, Franco Servadei, Lori Shutter, Deborah M. Stein, Nino Stocchetti, Fabio Silvio Taccone, Shelly D. Timmons, Eve C. Tsai, Jamie S. Ullman, Paul Vespa, Walter Videtta, David W. Wright, Christopher Zammit, Randall M. Chesnut

Bibliographic record

VenueMonash University Research Portal (Monash University) · 2019
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsOttawa HospitalUniversity of OttawaUniversity of Manitoba
FundersEuropean Society of Intensive Care MedicineNational Institute for Health and Care ResearchStrykerCongress of Neurological Surgeons
KeywordsMedicinePsychological interventionTraumatic brain injuryIntracranial pressureIntracranial pressure monitoringSedationAlgorithmIntensive care medicineMedical emergencyComputer scienceSurgeryPsychiatry

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)0.010
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0070.002
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0040.004
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0040.003

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 head0.030
GPT teacher head0.276
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations0
Published2019
Admission routes1
Has abstractno

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