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Record W2793563241 · doi:10.1155/2018/4765050

Pathology and Treatment of Traumatic Cervical Spine Syndrome: Whiplash Injury

2018· review· en· W2793563241 on OpenAlexaboutno aff
Nobuhiro Tanaka, Kıvanç Ateşok, Kazuyoshi Nakanishi, Naosuke Kamei, Toshio Nakamae, Shinji Kotaka, Nobuo Adachi

Bibliographic record

VenueAdvances in Orthopedics · 2018
Typereview
Languageen
FieldMedicine
TopicNeurosurgical Procedures and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTinnitusHeadachesWeaknessWhiplashNeck painPhysical therapyPoison controlSurgeryPsychiatryMedical emergencyAlternative medicinePathology

Abstract

fetched live from OpenAlex

Traumatic cervical syndrome comprises the various symptoms that occur as a result of external force such as that of a traffic accident. In 1995, the Quebec Task Force on whiplash-associated disorders (WAD) formulated the Quebec classification, with accompanying clinical practice guidelines. These guidelines were in accordance with the stated clinical isolated or combined symptoms of the syndrome: neck pain, headaches, dizziness, numbness of head or face, eye pain, vision loss, double vision, tinnitus, hearing loss, nausea, and numbness and/or weakness of extremities. In recent years, cerebrospinal fluid hypovolemia or fibromyalgia has been recognized as a major notable cause of a variety of symptoms, although many clinical questions remain regarding the pathology of this syndrome. Therefore, its diagnosis and treatment should be conducted extremely carefully. While the Quebec classification and its guidelines are very useful for the normalization and standardization of symptoms of traumatic cervical syndrome, in the future, we would like to see the emergence of new guidelines that better address the diversity of this disease.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.044
GPT teacher head0.380
Teacher spread0.336 · 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
GenreReview

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

Citations42
Published2018
Admission routes1
Has abstractyes

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