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Record W4295450568 · doi:10.2519/jospt.2022.11147

International Framework for Examination of the Cervical Region for Potential of Vascular Pathologies of the Neck Prior to Musculoskeletal Intervention: International IFOMPT Cervical Framework

2022· article· en· W4295450568 on OpenAlexaff
Alison Rushton, Lisa C. Carlesso, Timothy W. Flynn, Wayne Hing, Sidney M. Rubinstein, Steven Vogel, Roger Kerry

Bibliographic record

VenueJournal of Orthopaedic and Sports Physical Therapy · 2022
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineExpert opinionCervical spinePhysical examinationHead and neckIntervention (counseling)Physical therapyPsychological interventionPosition statementIntensive care medicinePhysical medicine and rehabilitationMedical physicsSurgeryFamily medicine

Abstract

fetched live from OpenAlex

SYNOPSIS: This position statement, stemming from the International IFOMPT (International Federation of Orthopaedic Manipulative Physical Therapists) Cervical Framework, was developed based upon the best contemporary evidence and expert opinion to assist clinicians during their clinical reasoning process when considering presentations involving the head and neck. Developed through rigorous consensus methods, the International IFOMPT Cervical Framework guides assessment of the cervical spine region for potential vascular pathologies of the neck in advance of planned interventions. Within the cervical spine, events and presentations of vascular pathologies of the neck are rare but are an important consideration as part of patient examination. Vascular pathologies may be recognizable if the appropriate questions are asked during the patient history–taking process, if interpretation of elicited data enables recognition of this potential, and if the physical examination can be adapted to explore any potential vasculogenic hypothesis. J Orthop Sports Phys Ther 2023;53(1):7–22. Epub: 14 September 2022. doi:10.2519/jospt.2022.11147

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)0.001
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 score0.897
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.014
GPT teacher head0.296
Teacher spread0.282 · 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 teacher head, 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

Citations92
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Orthopaedic and Sports Physical TherapySame topicCervical and Thoracic MyelopathyFrench-language works237,207