The performance of machine learning algorithm in surgical site infections case identification and prediction, a scoping review protocol (Addendum for DOI: 10.17605/OSF.IO/F8ERZ)
Bibliographic record
Abstract
Surgical site infections (SSI) are the most frequent reported healthcare associated infection among surgical patients[1, 2]. Annually, a total of 1.3 million operative procedures were performed in Canada, 2-5% of the patients acquired SSI[3, 4]. The cost associated with SSI are estimated up to $1 million each year in Canada[3]. The length of hospital stay was prolonged by an average of 11 days due to SSI, and the readmission rate for patients who experienced SSI are five times higher than the patient who did not experience SSI[3, 5]. Detecting SSI is an essential step of infection prevention and control programs to further develop quality initiatives to decrease the infection rates. Traditional methods of SSI case identification often require extensive human resources and time-consuming[6]. Machine learning (ML) algorithm which leveraging the enrich text data documented in electronic medical record (EMR) have been applied in SSI case identification and prediction[7-11], yet its effectiveness was not summarized. To close this knowledge gap, we will conduct a systematic review to scan the literature evidence of ML algorithm applied in SSI case identification/ prediction to summarize its overall performance.
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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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".