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 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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".