Cancellation of Surgeries by the Patient, Doctor or Institution: An Approach to Legal Ethical Aspects
Bibliographic record
Abstract
INTRODUCTION: The cancellation of surgery represents a dilemma in establishing relatively adequate cancellation rates according to the factor, because each institution and surgical specialty have different dynamics. Objective: Describe the types of factors present for the cancellation of surgeries in a health institution. Colombia (2017-2018). METHODOLOGY: Descriptive, retrospective, cross-sectional study. We reviewed (3339) records of scheduled surgeries from January to December 2017. In 2018 they were reviewed (1733) between January and June. A total of (5072) records of a Third Level Health Institution of the Department of Cesar / Colombia were reviewed. The Neuronal Multilayer Perceptron Network model and the Gini coefficient were applied to determine the most important factor and therefore the inequality between them. RESULTS: In 2017, there was a surgical cancellation rate of 4% of the total number of scheduled surgeries (3339). For the year 2018, the rate was 3% of the total of scheduled surgeries (1733). The most important factor was due to the patient's adverse conditions. The surgical specialties that had the highest number of cancellations were general surgery followed by orthopedics. CONCLUSION: An evaluation of the factors for the cancellation of programmed surgeries with a high coefficient of inequality is described. In addition, the most important factor was related to the patient. Prospective studies by specialty are proposed for the design of solution and monitoring strategies to avoid surgical cancellations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".