Queuing management study at the Multidisciplinary Anesthesia and Intensive Care Clinic of CNHU-HKM in April 2022
Bibliographic record
Abstract
Objective: Constant availability of inpatient beds in an intensive care unit (ICU) is part of the resilience of health systems, especially in an emergency context, namely in public health. This study aims to appraise the management of inpatient waiting lines in the ICU of Hubert Koutoukou Maga National Hospital and University Center (CNHU-HKM) in Benin, in March-April 2022.Methods: This was an analytic cross-sectional study of inpatients or their relatives and staff, selected by convenience and reasoned choice, respectively, carried out from March 21 to April 15, 2022. Logistic regression was used to identify associated factors with queues management.Results: Altogether 55 patients were surveyed. On a daily basis, 13 ± 1 patients were hospitalized in 18 functional beds for 3 ± 1 admissions and 3 ± 1 discharges. The average bed occupancy rate was 89.8% ± 3.8%; the average waiting time before patient care was 3.6 ± 1.2 minutes and the traffic intensity were 0.03. Per hour, the odds of having a patient were 33.29%, with a 97% chance of a bed being occupied. The probability that an admitted patient would spend a whole week there was 37%. Only patient arrival flow was significantly associated with insufficient queuing management. There was also a lack of inpatient beds and technical boards. The construction of two wards and the installation of seven additional beds could improve queues management.Conclusions: The management of AF in our study site depends mainly on the daily flow of arriving patients, but also on the number of available hospital beds, the working organization and the existing technical and structural measures. Addressing these parameters will significantly improve the situation.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".