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Record W2553469580 · doi:10.1177/1460408616676503

The development of a threshold curve for the understanding of concussion in sport

2016· article· en· W2553469580 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTrauma · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsOttawa HospitalSt. Michael's HospitalUniversity of Ottawa
Fundersnot available
KeywordsConcussionMedicineContext (archaeology)Traumatic brain injuryHead injuryPhysical medicine and rehabilitationPoison controlInjury preventionSurgeryMedical emergency

Abstract

fetched live from OpenAlex

Much of what is known concerning human brain injury thresholds is based upon impacts to cadavers and animal models that were used to generate the Wayne State Concussion Tolerance Curve (WSTC) and similar curves. These curves are the foundation for predictive metrics used in standard development as well as helmet design. These curves were based upon a very narrow range of impacts; impacts whose characteristics differ greatly from how the head is impacted in sport. This research examines the uses of time-based curves like the WSTC in the context of understanding mechanisms of brain injury and head protection. Published linear/rotational acceleration magnitude/duration data from Hybrid III laboratory reconstructions of brain injury events were plotted. This research further develops the understanding of injury thresholds in comparison to threshold curves such as the WSTC and Brain Injury Curve Leuven. The data demonstrate the relationships between magnitude and duration of dynamic response on minor traumatic brain injury (mTBI) in sport.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.096

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.212
GPT teacher head0.385
Teacher spread0.172 · 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