Incidence and Reasons for a Surgical Cancellation at a Hospital in Rwanda
Bibliographic record
Abstract
Background: Surgery cancellation is a challenging and costly event resulting in operating theatre inefficiency and psychological and financial problems for the patients and their families. This study aimed to find out the incidence and reasons for surgical cancellation at a Rwandan hospital. Methods: A retrospective study was conducted on 736 patients' files obtained from theatre registry lists of surgical operations done from January to March 2017. The American Association of Perioperative Nurses (AORN) checklist for documenting cancelled surgical cases was used to establish the rate and reasons for cancellation. Data were analyzed using frequency and percentage descriptive statistics. Results: Out of the 736 surgeries booked, 179 (24.3%) were cancelled as follows: Orthopedic and general surgeries (28.2%) respectively, gynecology and obstetrics (27.4%), urology surgeries (15.5%), maxillofacial surgeries (15.9%), ENT (15.6%) and plastic surgeries (13.3%). Time constrain/long list (19.6%), acute change in medical status (10.6%), non-turn-up of the patient (8.4%), and abnormal lab findings (7.8%) were the most prevalent reasons. Conclusion: The surgical cancellation rate at the study hospital was 24%, increasing with the number of patients booked and the type of surgical procedure. A prospective study is required to gain more insight into the reason for cancellations, mostly amenable to mitigation measures.
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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 | 0.005 |
| 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 | 0.000 |
| 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".