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Record W6907768696 · doi:10.25384/sage.21564990

sj-docx-1-jcn-10.1177_08830738221120741 - Supplemental material for A Scoping Review of Magnetic Resonance Modalities Used in Detection of Persistent Postconcussion Symptoms in Pediatric Populations

2022· article· en· W6907768696 on OpenAlexaff

Bibliographic record

VenueSage Journals Data · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsModalitiesMagnetic resonance imagingNeurologyTherapeutic modalitiesScratchMEDLINE

Abstract

fetched live from OpenAlex

Supplemental material, sj-docx-1-jcn-10.1177_08830738221120741 for A Scoping Review of Magnetic Resonance Modalities Used in Detection of Persistent Postconcussion Symptoms in Pediatric Populations by Elena Sheldrake, Brendan Lam, Hiba Al-Hakeem, Anne L. Wheeler, Benjamin I. Goldstein, Benjamin T. Dunkley, Stephanie Ameis, Nick Reed and Shannon E. Scratch in Journal of Child Neurology

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)0.003
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.053
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.043
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.012
Science and technology studies0.0010.001
Scholarly communication0.0060.006
Open science0.0030.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.9470.643

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 head0.133
GPT teacher head0.399
Teacher spread0.266 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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