P019: What happens to John Doe? Unidentified patients in the emergency department: a retrospective chart review
Bibliographic record
Abstract
Introduction: Patients who are not identified upon presentation to the emergency department (ED), commonly referred to as John or Jane Does (JDs), are a vulnerable population due to the sequelae associated with this lack of patient information. To date, there has been minimal research describing JDs. We aimed to characterize the JD population and determine if it differs significantly from the general ED population. Methods: We conducted a retrospective chart review of 114 JDs admitted to Saskatoon EDs from May 2018 to April 2019. Patients met inclusion criteria if they were provided a unique JD identification number at ED admission because their identities were unknown or unverifiable. Data regarding demographics, clinical presentation, ED course, mode of identification, and major clinical outcomes (i.e. admission rates, mortality rates) were gathered from electronic records. A second reviewer abstracted a random 21.0% sample of charts to ensure validity of the data. The JD population was then compared to the general population of ED patients that presented during the same time period. Results: Male JDs most commonly presented as trauma activations (85.7%) in contrast to female JDs who most commonly presented with issues related to substance abuse (51.4%). Compared to the general ED population, a greater percentage of JDs were categorized as CTAS 1 or 2 (85.8% vs 18.9%, p < 0.0001), more likely to be 44 years of age or younger (82.4% vs 58.5%, p < 0.0001), and more likely to be male (64.9% vs 49.1%, p < 0.0001). Descriptive statistics on the JD population demonstrated that most JDs received consults to inpatient services (58.8%). Of JDs who presented to the ED, 34.2% were admitted to hospital. The mortality of the JD population was 13.2% at 3 months. The ED average (SD) length of stay for JDs was 8.7 (9.0) hours. How JDs were ultimately identified was recorded only 70.2% of the time. Most frequently, JDs identified themselves (26.3%), other identification methods included police services (14.9%), family members (7.9%), registered nurses (6.1%), government-issued identification (5.3%), social work (4.4%) or other measures (5.4%). Conclusion: JD's represent a unique population in the ED. Both their presentations and clinical outcomes differ significantly from the generalized ED population. More research is needed to better identify strategies to improve the management and identification methods of these unique patients.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".