Estimation of unregistered patients who left without being seen
Bibliographic record
Abstract
Abstract Objective To determine whether changes to the appearance of an emergency department (ED) waiting room influenced the number of patients who left without being seen (LWBS). Design Retrospective analysis using National Ambulatory Care Reporting System data collected at the time of patient registration. Setting The ED of Belleville General Hospital, a mid-sized secondary care community hospital in Ontario with a catchment population of 125 000. Participants All unscheduled patients registering at the hospital to be seen in the ED from July 1 to December 31, 2016 (control period), and from July 1 to December 31, 2017 (study period). Main outcome measures The volume of patients registering by Canadian Triage and Acuity Scale (CTAS) level to be seen in the ED during the study period compared with the volume of patients registering during the control period, and the number of LWBS during the 2 time periods. Results The average number of patients registered per month was significantly greater in the study period than in the control period (t10 = -5.53, P < .01). A total increase of 1881 registrations was recorded in the study period, or 10.47% (increase per month ranged from 9.59% to 11.66%). The proportion of patients with less acute triage scores decreased in the study period; however, the differences in CTAS levels between the 2 years was not statistically significant (χ2 = 1.05, P = .90). The number of LWBS according to CTAS level was lower in all categories in the study period, including those in the less acute levels, decreasing from 60 in CTAS 5 in 2016 to 45 in 2017, and 585 in CTAS 4 in 2016 to 330 in 2017. Overall, the distribution of LWBS by CTAS level was significantly different between the control and study periods (P < .01). Conclusion The number of patients registering is influenced by the apparent high or low occupancy of the waiting area at the time of registration.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".