Orthopaedic Surgery Complications at a Tertiary Care Hospital in a Low- and Middle-Income Country: A National Surgical Quality Improvement Project Analysis
Bibliographic record
Abstract
Background: Through a comparison of orthopaedic surgical procedures performed at a tertiary care hospital in Pakistan with other participating hospitals of National Surgical Quality Improvement Project (NSQIP), we aim to identify the areas of orthopaedic surgical care at our center that need improvement and also those which are at par with international standards. Methods: The study analyses orthopaedic surgeries at a tertiary care hospital in Pakistan using NSQIP registry to compare complication rates with other American College of Surgeons-NSQIP participant hospitals. Two reviewers collect data in different days every 8 days to reduce bias, and the results are reported in odds ratios using quarterly reports. This study included 584 eligible orthopaedic cases performed in 2021. Yearly institutional reports with odds ratios were also used to identify areas needing improvement and to implement changes to improve orthopaedic surgical outcomes at said institute. Results: The quarterly reports suggest a relatively higher OR for certain indicators such as cardiac events, surgical site infection, mortality, and morbidity throughout. The renal failure rate was very high in the third and fourth quarters with odds ratios of 4.57 and 10.31, respectively. However, the official NSQIP annual institutional report for 2021 identified sepsis, surgical site infections, and cardiac complications as areas ‘needing improvement’. It also indicated that the hospital performed exemplarily when it came to venous thromboembolism (VTE). As for the rest of the indicators, the hospital fell in the ‘as expected’ category of the NSQIP standards. Conclusion: This initial report helps the hospital's orthopaedic department in recognizing areas for improvement and making…
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".