Trends in osteoporotic fracture and related in-hospital complications during the COVID-19 pandemic in Alberta, Canada
Bibliographic record
Abstract
Fragility fractures (i.e., low-energy fractures) account for most fractures among older Canadians and are associated with significant increases in morbidity and mortality. Study results suggest that low-energy fracture rates (associated with surgical intervention and outcomes) declined slightly, but largely remained stable in the first few months of the COVID-19 pandemic. PURPOSE/INTRODUCTION: This study describes rates of low-energy fractures, time-to-surgery, complications, and deaths post-surgery in patients with fractures during the coronavirus disease (COVID-19) pandemic in Alberta, Canada, compared to the three years prior. METHODS: A repeated cross-sectional study was conducted using provincial-level administrative health data. Outcomes were assessed in 3-month periods in the 3 years preceding the COVID-19 pandemic and in the first two 3-month periods after restrictions were implemented. Patterns of fracture- and hospital-related outcomes over the control years (2017-2019) and COVID-19 restrictions periods (2020) were calculated. RESULTS: Relative to the average from the control periods, there was a slight decrease in the absolute number of low-energy fractures (n = 4733 versus n = 4308) during the first COVID-19 period, followed by a slight rise in the second COVID-19 period (n = 4520 versus n = 4831). While the absolute number of patients with low-energy fractures receiving surgery within the same episode of care decreased slightly during the COVID-19 periods, the proportion receiving surgery and the proportion receiving surgery within 24 h of admission remained stable. Across all periods, hip fractures accounted for the majority of patients with low-energy fractures receiving surgery (range: 58.9-64.2%). Patients with complications following surgery and in-hospital deaths following fracture repair decreased slightly during the COVID-19 periods. CONCLUSIONS: These results suggest that low-energy fracture rates, associated surgeries, and surgical outcomes declined slightly, but largely remained stable in the first few months of the pandemic. Further investigation is warranted to explore patterns during subsequent COVID-19 waves when the healthcare system experienced severe strain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".