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Record W4401872970 · doi:10.1016/j.jts.2024.07.006

Fiche synthèse pour l’utilisation des outils SCAT6® et SCOAT6

2024· article· fr· W4401872970 on OpenAlexaff
S. Leclerc, Camille Tooth, A. Thibaut, G. Martens, A. Bwenge, Pierre Frémont, J.-F. Kaux

Bibliographic record

VenueJournal de Traumatologie du Sport · 2024
Typearticle
Languagefr
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsUniversité LavalMontreal Heart Institute
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

La fiche synthèse pour l’utilisation des outils SCAT6® et SCOAT6 aide les cliniciens à comprendre et utiliser ces outils recommandés par la 6 e conférence de consensus sur les commotions cérébrales de 2022. Les commotions cérébrales, blessures courantes mais graves, nécessitent une prise en charge rapide. Le SCAT6® ( Sport Concussion Assessment Tool ) et le SCOAT6 ( Sport Concussion Office Assessment Tool ) fournissent des directives claires pour l’évaluation immédiate sur le terrain, le suivi à court terme et la gestion à long terme des commotions cérébrales. Cette fiche décrit l’utilisation de ces outils pour différentes situations cliniques, de l’évaluation initiale à la prise en charge post-commotionnelle, incluant des suggestions thérapeutiques adaptées. The summary sheet for the use of SCAT6® and SCOAT6 tools helps clinicians understand and use these tools, recommended by the 6th Concussion Consensus Conference of 2022. Concussions are common but serious injuries that require prompt treatment. The SCAT6® (Sport Concussion Assessment Tool) and SCOAT6 (Sport Concussion Office Assessment Tool) provide clear guidelines for immediate field assessment, short-term follow-up and long-term management of concussions. This sheet describes the use of these tools for different clinical situations, from initial assessment to post-concussion management, including appropriate therapeutic suggestions.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.298
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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2024
Admission routes1
Has abstractyes

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