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Record W4414147166 · doi:10.1101/2025.09.07.25335026

Pathology Testing for Patients With Low Back Pain in Australian Emergency Departments

2025· preprint· en· W4414147166 on OpenAlexaff
Claudia Côté‐Picard, Qiuzhe Chen, Hugo Massé‐Alarie, Peter Youssef, Michael C. Spies, Christopher G. Maher, Gustavo C Machado

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicHematological disorders and diagnostics
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
FundersNational Health and Medical Research CouncilMedical Research CouncilUniversity of SydneyArthritis Australia
KeywordsEmergency departmentTriageLow back painLumbarOdds ratioOddsGuidelineLogistic regression

Abstract

fetched live from OpenAlex

OBJECTIVE: We aimed to describe the profile of patients with low back pain who received pathology testing in emergency departments or after inpatient admission, and to describe the ordered tests. METHODS: A retrospective study of electronic medical records from three emergency departments in Sydney, New South Wales, Australia, from January 2016 to October 2021, was undertaken. We included patients diagnosed with a lumbar spine condition at discharge from the emergency department and extracted their demographic and episode of care characteristics. RESULTS: Pathology tests were ordered in 23.8% of 15,300 episodes of care. Patients who received pathology testing were typically older, were female, had a non-English preferred language, required an interpreter, arrived by ambulance during working hours, had their condition triaged as urgent, had a diagnosis of a serious low back pain pathology, were admitted to inpatient wards, and had an increased length of stay. Full blood count, electrolytes, urea, creatinine (EUC) and liver function tests were the most ordered tests. CONCLUSIONS: The characteristics of patients with low back pain receiving pathology testing in emergency departments as well as those of the tests were described. The results suggest that guideline recommendations were partly followed, but an investigation into the appropriateness of pathology testing is needed to confirm this hypothesis and ensure the relevance of low back pain care in emergency departments.

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)0.000
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.035
GPT teacher head0.308
Teacher spread0.272 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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