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Record W3013006036 · doi:10.1186/s12913-020-05146-0

Medical and allied health service use during acute and chronic post-injury periods in whiplash injured individuals

2020· article· en· W3013006036 on OpenAlexaff
Carrie Ritchie, Ashley Smith, Michele Sterling

Bibliographic record

VenueBMC Health Services Research · 2020
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Calgary
FundersInstitute for Safety, Compensation and Recovery ResearchWorkSafe VictoriaMonash UniversityTransport Accident Commission
KeywordsMedicineChiropracticPopulationWhiplashHealth administrationPhysical therapyOccupational safety and healthNeck painPoison controlChronic painPublic healthEmergency medicineNursingAlternative medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals with whiplash associated disorder (WAD) frequently experience neck pain in addition to other physical, psychological and social symptoms. Consequently, treatment is sought from a variety of health professionals. The limited data available about health services use in this population are conflicting. This study aimed to characterise health service use in individuals with WAD from a motor vehicle crash. METHODS: Medical (general practitioner (GP), medical specialist, emergency services (ED), radiology - x-ray, computed tomography, magnetic resonance imaging, ultrasound) and allied health service (physiotherapy, chiropractor, psychologist, osteopath, occupational therapy) use during acute (< 12 weeks) and chronic (12 weeks to 2 years) post-injury periods were analysed in adults claiming compensation for WAD in the no-fault jurisdiction of Victoria, Australia (n = 37,315). RESULTS: Most WAD claimants had an acute post-injury health service payment (95%, n = 35,348), and approximately one-third (29%, n = 10,871) had a chronic post-injury health service payment. During an acute post-injury period, the most frequently compensated services were for: ED (82% of acute claimants), radiology (56%), and medical specialist (38%). Whereas, physiotherapy (64.4% of chronic claimants), GP (48.1%), and radiology (34.6%) were the most frequently paid services during the chronic period. Females received significantly more payments from physiotherapists (F = 23.4%, M = 18%, z = - 11.3, p < .001, r = 0.13), chiropractors (F = 7.4%, M = 5.6%, z = - 6.3, p < .001, r = 0.13), and psychologists (F = 4.2%, M = 2.8%, z = - 6.7, p < .001, r = 0.18); whereas, males received significantly more medical services payments from medical specialists (F = 41.8%, M = 43.8%, z = - 3.7, p < .001, r = 0.03), ED (F = 74.0%, M = 76.3%, z = - 4.9, p < .001, r = 0.03) and radiology (F = 58.3%, M = 60.1%, z = - 3.4, p < .001, r = 0.02). CONCLUSIONS: Individuals with WAD claimed for a range of health services. Radiology imaging use during the acute post-injury period, and physiotherapy and chiropractor service use during the chronic post-injury period appeared concordant with current WAD management guidelines. Conversely, low physiotherapy and chiropractic use during an acute post-injury period, and high radiology and medical specialists use during the chronic post-injury period appeared discordant with current guidelines. Strategies are needed to help inform medical health professionals of the current guidelines to promote early access to health professionals likely to provide an active approach to treatment, and to address unnecessary referral to radiology and medical specialists in individuals with on-going WAD.

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)0.002
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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.037
GPT teacher head0.418
Teacher spread0.381 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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