Pathology Testing for Patients With Low Back Pain in Australian Emergency Departments
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".