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Record W2087044966 · doi:10.1136/bmj.f1757

Predictive value of S-100  protein for prognosis in patients with moderate and severe traumatic brain injury: systematic review and meta-analysis

2013· review· en· W2087044966 on OpenAlexafffund
Éric Mercier, Amélie Boutin, François Lauzier, Dean Fergusson, Jean-François Simard, Ryan Zarychanski, Lynne Moore, Lauralyn A. McIntyre, Patrick Archambault, François Lamontagne, France Légaré, Ed Randell, Line Nadeau, François Rousseau, AF Turgeon

Bibliographic record

VenueBMJ · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicS100 Proteins and Annexins
Canadian institutionsMemorial University of NewfoundlandUniversité de SherbrookeUniversity of ManitobaOttawa HospitalHôpital de l'Enfant-JésusUniversity of OttawaUniversité Laval
FundersCanadian Institutes of Health ResearchUniversité LavalCanadian Anesthesia Research Foundation
KeywordsTraumatic brain injuryMeta-analysisPredictive valueMedicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine the ability and accuracy of the S-100β protein in predicting prognosis after a moderate or severe traumatic brain injury. DESIGN: Systematic review and meta-analysis of randomised controlled trials and observational studies. DATA SOURCES: Medline, Embase, Cochrane Central Register of Controlled Trials, BIOSIS (from their inception to April 2012), conference abstracts, bibliographies of eligible articles, and relevant narrative reviews. STUDY SELECTION: Two reviewers independently reviewed citations and selected eligible studies, defined as cohort studies or randomised control trials including patients with moderate or severe traumatic brain injury and evaluating the prognostic value of S-100β protein. Outcomes evaluated were mortality, score on the Glasgow outcome scale, or brain death. DATA EXTRACTION: Two independent reviewers extracted data using a standardised form and evaluated the methodological quality of included studies. Pooled results were presented with geometric means ratios and analysed with random effect models. Prespecified sensitivity analyses were performed to explain heterogeneity. RESULTS: The search strategy yielded 9228 citations. Two randomised controlled trials and 39 cohort studies were considered eligible (1862 patients). Most studies (n=23) considered Glasgow outcome score ≤ 3 as an unfavourable outcome. All studies reported at least one measurement of S-100β within 24 hours after traumatic brain injury. There was a significant positive association between S-100β protein concentrations and mortality (12 studies: geometric mean ratio 2.55, 95% confidence interval 2.02 to 3.21, I(2)=56%) and score ≤ 3 (18 studies: 2.62, 2.01 to 3.42, I(2)=79%). Sensitivity analysis based on sampling time, sampling type, blinding of outcome assessors, and timing of outcome assessment yielded similar results. Thresholds for serum S-100β protein values with 100% specificity ranged from 1.38 to 10.50 µg/L for mortality (six studies) and from 2.16 to 14.00 µg/L for unfavourable neurological prognosis as defined by the Glasgow outcome score. CONCLUSIONS: After moderate or severe traumatic brain injury, serum S-100β protein concentrations are significantly associated with unfavourable prognosis in the short, mid, or long term. Optimal thresholds for discrimination remain unclear. Measuring the S-100β protein could be useful in evaluating the severity of traumatic brain injury and in the determination of long term prognosis in patients with moderate and severe injury.

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)0.021
metaresearch head score (Gemma)0.057
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 score0.024
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.057
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0240.040
Bibliometrics0.0100.010
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.330
Teacher spread0.282 · 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

Citations131
Published2013
Admission routes2
Has abstractyes

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