Current management practices for patients presenting with low back pain to a large emergency department in Canada
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is one of the leading causes of disability. Presentations to the emergency department (ED) are common and consume significant healthcare resources. However, treatment of patients with LBP is variable and highly physician dependent. Our study objective was to describe the demographic and clinical characteristics of patients presenting to the ED with LBP, the diagnostic strategies employed by ED physicians, and the subsequent management. METHODS: We conducted a retrospective study using clinical and electronic health data at the Queen Elizabeth II Health Science Center's Charles V. Keating Emergency and Trauma Centre. We selected a simple random sample of 325 adult participants who presented to the ED with non-urgent LBP over a six-year period. Data for all participants, including demographic characteristics, diagnostic testing, and interventions received, was retrieved from the Emergency Department Information System database and from patient charts. RESULTS: Participants had a median age of 43 years and 55% were female. The majority (92.9%) were acute presentations of LBP (less than 4 weeks of duration), with an assigned Canadian Triage Acuity Scale score of 3-4 (92.4%). A range of pain intensity scores were reported, mostly without associated neurological symptoms (81%) or sciatica (68%). At triage, pain score was most commonly reported as moderate intensity (57.6%), followed by severe (32.6%) and mild (9.9%). Documentation of pain rating during assessment was similar (moderate 68.6%; severe 25.9%; mild 5.6%). Laboratory investigations were conducted on 22.5% of participants and 30% received an imaging study. Medications were delivered to 59.4% of participants during their stay in the ED. Of the medications administered, ibuprofen (28.3%), hydromorphone (24.9%), and acetaminophen (21.5%) were the most frequent. Almost all (94%) had a record of having a primary care provider in EDIS and referrals back to the participant's family physician were recorded for 41.2% of non-urgent LBP encounters. CONCLUSIONS: We presented a complete description of patient characteristics, LBP descriptors, and health service use for a random sample of non-urgent LBP patients presenting to the ED. This has allowed for a better understanding of patients who seek care in the ED for their non-urgent LBP.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".