MétaCan
Menu
← Back to cohort
Record W4226343247

Concussion knowledge among North American chiropractors.

2021· article· en· W4226343247 on OpenAlexaffabout
Mohsen Kazemi, Kevin Rajin Deoraj, Milcah Hiemstra, Lauren Kimberly Santiago

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCanadian Memorial Chiropractic College
Fundersnot available
KeywordsChiropracticConcussionMedicineFamily medicinePoison controlInjury preventionAlternative medicineEmergency medicinePathology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: To investigate the degree of knowledge North American chiropractors have in regards to concussion diagnosis and management. METHODS: A Concussion Knowledge Assessment Tool (CKAT) survey was administered to North American chiropractors through SurveyMonkey.com. This survey was sent to all practicing members of the American Chiropractic Association (ACA) and Canadian Chiropractic Association (CCA). RESULTS: 1321 surveys were completed and analyzed (response rate of 3.3%). The average score of the CKAT amongst North American Chiropractors was 4.82 out of 9. Using our modified scoring method, chiropractors scored 39.44 out of 48. CONCLUSIONS: North American chiropractors who participated in this study demonstrated concussion knowledge and management using the CKAT tool. Further investigation is recommended in order to address learning gaps and updating the CKAT based on current literature and guidelines.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.067
GPT teacher head0.318
Teacher spread0.251 · 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 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
Published2021
Admission routes2
Has abstractyes

Explore more

Same venuePubMed→Same topicTraumatic Brain Injury Research→French-language works237,207→