Defining and Characterizing Visits To Emergency Departments For Musculoskeletal Conditions: A Retrospective Analysis Using Two Publicly Available Databases
Bibliographic record
Abstract
Abstract Background The number of people with musculoskeletal conditions who visit the ED is estimated to range from 3-25% of all ED visits, challenging health service planning. The aim of this study was to examine how using different ICD code lists to define musculoskeletal conditions affected estimates for the number of people with musculoskeletal conditions who visit the ED. Methods In this cross-sectional study, we compared three different ICD code lists to see whether they provided different answers to the question: “What proportion of ED visits are due to musculoskeletal conditions?”. Data were from two publicly available databases: the Medical Information Mart for Intensive Care IV Emergency Department database and the California Department of Health Care Access and Information Hospital Emergency Department database. The total number of (a) ED visits and (b) potentially avoidable ED visits were estimated for the three code lists. Data are presented descriptively. Results Visits for musculoskeletal conditions accounted for between ∼6% to ∼18% of all ED visits, depending on the ICD code list used to identify visits. Between ∼6% to ∼8% of all ED visits were potentially avoidable visits for musculoskeletal conditions. Conclusion There were larger differences in the total number of ED visits for musculoskeletal conditions identified by the different code lists than in the total number of potentially avoidable ED visits for musculoskeletal conditions. To accurately quantify and characterize patients with musculoskeletal conditions who present to the ED, researchers must first validate the criteria used to define musculoskeletal conditions.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".