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Record W2141919224 · doi:10.1186/cc10905

Predictive value of glial fibrillary acidic protein for prognosis in patients with moderate and severe traumatic brain injury: a systematic review and meta-analysis

2012· review· en· W2141919224 on OpenAlexaff
Éléna Laroche, AF Turgeon, Amélie Boutin, Éric Mercier, François Lauzier, Ryan Zarychanski, Lynne Moore, Patrick Archambault, François Lamontagne, François Rousseau, France Légaré, Ed Randell, J.‐M. Lapointe, J. Lacroix, Dean Fergusson

Bibliographic record

VenueCritical Care · 2012
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsMemorial University of NewfoundlandUniversité de SherbrookeUniversity of TorontoOttawa HospitalUniversity of ManitobaUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsGlial fibrillary acidic proteinTraumatic brain injuryMedicinePredictive valueInternal medicinePathologyImmunohistochemistryPsychiatry

Abstract

fetched live from OpenAlex

Biomarkers have been proposed as potential prognostic indicators following a traumatic brain injury (TBI). Among those, glial fibrillary acidic protein (GFAP) has been one of the most studied. The objective of this study was to assess the prognostic value of GFAP levels in patients with moderate to severe 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 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)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.016
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.349
Teacher spread0.273 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations3
Published2012
Admission routes1
Has abstractyes

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