IMPLEMENTATION AND PERFORMANCE OF TRACKERS FOR THE DETECTION OF SURGICAL ADVERSE EVENTS
Bibliographic record
Abstract
ABSTRACT Objective: to identify the frequency and performance of the Canadian Adverse Events Study tracking criteria for the confirmation of surgical adverse events in adult patients. Method: a descriptive and retrospective study conducted in a public hospital in the state of Paraná from May to November 2017. A retrospective review of 192 medical records was conducted using 16 tracking criteria; and the confirmation of adverse events was in charge of a committee of experts composed of a physician and nurses. Data was analyzed by means of descriptive statistics. Results: the mean performance of the trackers was 73.3%. A total of 70 trackers were confirmed in 21.8% of the medical records with adverse events. The mean number of trackers was 0.4 per medical record (varying from zero to three). Adverse reaction to the medication; unplanned return to the operating room; unplanned removal, injury or correction of an organ or structure during surgery or invasive procedure; cardiopulmonary arrest reversed and hospital infection/sepsis were classified as high performance trackers (100.0%). Eight trackers did not contribute to the identification of adverse events. Conclusion: high-performance trackers can assist in detecting adverse events; there is potential to improve the tracking tool, contributing to its performance as a research method in Brazilian hospitals.
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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".