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Record W2982929158

Classification, care-seeking behaviour and pre-hospital triage of patients exposed to whiplash trauma

2019· article· en· W2982929158 on OpenAlexaboutno aff
Artur Tenenbaum

Bibliographic record

VenueGothenburg University Publications Electronic Archive (Gothenburg University) · 2019
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTriageWhiplashMedicineMedical emergencyTrauma careEmergency medicinePoison control
DOInot available

Abstract

fetched live from OpenAlex

Classification, care-seeking behaviour and pre-hospital triage of patients exposed to a whiplash trauma\nArtur Tenenbaum\nAvdelning för samhällsmedicin och folkhälsa, Institutionen för medicin, Sahlgrenska\nakademin, Göteborgs universitet, Sverige.\nAbstract\nKnowledge about the optimal first acute care level and classification after exposure to a whiplash trauma is important for the injured individual and for the healthcare system. Neck pain is ranked as the fourth most important condition in the “Global Burden of Disease Study”.\nExposure to a whiplash trauma is common and many individuals seek health care. Up to 50% of those with symptoms after whiplash trauma, labelled whiplash associated disorders (WAD), face chronic health problems.\nThe general aim of this thesis was to explore allocation of rehabilitation resources after a whiplash trauma by investigating if a Swedish classification model could be used as a complement to the Quebec Classification. Furthermore, to study gender differences in careseeking behavior immediately after whiplash trauma. A subsequent goal was to develop a risk\nstratification model for individuals exposed to whiplash trauma, a practical tool for medical personnel in prehospital triage after a neck trauma that results in neck pain. A prospective study was performed on 85 patients with WAD classified according to a new proposed classification system. Ten years of data from a database of injuries with more than 3000 patients exposed to a\nwhiplash trauma were used to construct an algorithm recommending the appropriate first level of care. Finally, a survey to 188 medical practitioners exploring their recommendations for prehospital triage of patients exposed to a traffic accident resulting in neck pain. Patients with\nwhiplash-associated disorders grade II and neuropsychological symptoms seem to have a worse prognosis for spontaneous recovery than those without. A Swedish classification system seems to be a complement to the Quebec classification. Women sought healthcare later than men after a whiplash trauma who sought hospital emergency department more often than women. Half of\nall individuals sought care at a hospital where only 6.4 % were hospitalized, while the other half sought care at a primary health care centre. Four risk factors were identified in patients diagnosed with WAD to predict the presence of a potentially sinister injury requiring hospital care; commotio cerebri, fracture or luxation, serious injury, and attending health care the same day as trauma. An algorithm recommending the appropriate first level of care was made. A\nconsensus around initial pre-hospital triage of patients with a very low or very high risk for sinister injury exist. This consensus correlates well to recent findings recommending appropriate\npre-hospital triage and first level of care.\nConclusion: The right level of care and classification after whiplash trauma is important for the injured individual and for the healthcare system.

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)0.004
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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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