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Record W4414847531 · doi:10.1038/s44303-025-00114-x

Author Correction: Aldehydic load as an objective imaging biomarker of mild traumatic brain injury

2025· erratum· en· W4414847531 on OpenAlexaff
Alexia Kirby, Cian Ward, Clara S. Goulet, Ryan Daniel, Joseph Wai-Hin Leung, Ashwin Sharma, Mojmı́r Suchý, Cassandra M. Donatelli, Jing Wang, Emily M. Standen, Adam J. Shuhendler

Bibliographic record

Venuenpj Imaging · 2025
Typeerratum
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsTraumatic brain injuryBiomarkerImaging biomarkerNeuroimaging

Abstract

fetched live from OpenAlex

The wrong figure 2 was originally published with this article; it has now been replaced with the correct one. Also, reference 12 is to be removed, and reference 107 is to be replaced by reference 66. The original article has been corrected.

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)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.049
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0030.001
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0360.027

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 head0.031
GPT teacher head0.378
Teacher spread0.347 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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