The burden of flashes and floaters in traditional general emergency services and utilization of ophthalmology on-call consultation: a cross-sectional study
Bibliographic record
Abstract
PURPOSE: To characterize the healthcare utilization and clinical characteristics of patients presenting with flashes and/or floaters (F/F) in general emergency service (GES) settings. METHODS: All adults presenting to GESs (emergency departments (EDs) and urgent care centers (UCCs)) with symptoms of F/F in Hamilton, Ontario between Jan. 1 - Dec. 31, 2018 were reviewed. Primary outcome was the proportion of patients presenting to GESs with F/F for which ophthalmology emergency services (OESs) were consulted. Secondary outcomes included features predictive of OES consultation by logistic regression and cost of GES utilization. RESULTS: Of 6590 primary eye-related visits to GESs, 10.4% (687) involved symptoms of F/F. Mean age of patients with F/F was 57 ± 15 years, and 61% were female. Consultation rate to OESs for F/F presentations was 89% (608/687). Logistic regression identified symptoms ≤ 2 weeks (OR 8.0; 95% CI 2.3-28), ≥ 45 years age (OR 2.4; 95% CI 1.4-4.3), UCC setting (OR 2.7; 95% CI 1.6-4.6), headache (OR 0.22; 95% CI 0.12-0.41), and neurologic symptoms (OR 0.1; 95% CI 0.19-0.49) as variables predictive of OES consultation. Mean time from triage to discharge in GESs for F/F patients was 2.43 ± 2.36 h. Mean cost per visit was $139.11 ± $113.93 Canadian dollars. Patients for which OES were consulted waited a total of 1345 h in GESs and accounted for $81,879.70 in costs. CONCLUSION: Patients presenting with F/F in GESs consume considerable resources in healthcare expenditure and time spent in GESs and most receive OES consultation. Identifying these patients at triage may allow for increased efficiency for the healthcare system and patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".