Assessing the waiting time for emergency orthopedic surgery for open fractures – A 6-month review of records at one of the largest referral public hospitals in Rwanda
Bibliographic record
Abstract
INTRODUCTION: The delay in surgical intervention for open fractures can have severe negative consequences. However, the delay for patients with open fractures presenting at Centre Hospitalier Universitaire de Kigali (CHUK), one of the largest public hospitals in Kigali, Rwanda, had not been studied. This study assessed the waiting time for surgery and compared it against the 6-hour (ideal time) and 24-hours (acceptable time) standards.METHODS: A review of the postoperative register and patients’ records was conducted. All medical charts of open fracture cases between April and September 2018 were audited. A surgical case was considered significantly delayed if the time interval from patient arrival at the emergency room to the operation theater was longer than 24 hours. The demographics, acuity level, insurance status and work shifts, were assessed using bi- and multivariate analysis.RESULTS: A total of 115 open fracture case files were audited. From arrival at the emergency room to surgery, the median time was 41 hours (IQR 21, 93). Only 3 (2.6%) were operated within 6 hours and 38 (33%) within 6 to 24 hours. The main factor contributing to the delay was obtaining orthopedic consultation note and documenting the decision to operate (median 10 hours, IQR 4 to 17). Meanwhile, the designated emergency theater was not utilized for a total of 18 hours per day, especially during night shifts.CONCLUSION: There was a significant delay in obtaining emergency orthopedic consultation and, thus, the timing of the surgical treatment. Examining the patient flow system in orthopedic surgical care delivery is needed in order to maximize theater utilization at this urban university hospital.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".