Assessment of Daily Burden and Factors for Overcrowded Emergency Department at Tertiary Care Hospital of Karachi
Bibliographic record
Abstract
Background: When in emergency room there is no enough area left to serve or to admit the subsequent sick patients who may require urgent attention and observation the setting is called as the overcrowded emergency room. Due to overcrowded emergency department the quality of services provided by the staff and doctors is compromised ultimately patients with severe diseases are ignored and this may be one of the causes for causalities. Objective: To assess the daily burden and factors responsible for overcrowding at emergency department of tertiary care hospital of Karachi. Methodology: It was a cross sectional study conducted at tertiary care hospital of Karachi from October 2020 to January 2021. Data of patients coming to adult emergency department of either gender were collected. Patients age <14 were excluded as these were referred to pediatric emergency department. Data collection was done according to Canadian emergency department triage and acuity scale (CTAS). Results: Total number (N) of patients who visited emergency department in study duration was 13434. The mean number of patients who visited ED was 141±13during our study duration. There was no any significant difference in presenting complaint. Delay in investigations was found to be a reason of prolong stay and overcrowding in ED in our setting. Conclusion: Overcrowding of patients in our ED of our setting was a common problem. The number of staff, doctors and beds were not matching the number of patient flow in the department. The main reason of prolong stay in ED was delay in investigations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".