Prediction of complications in spine surgery using machine learning: a Health 4.0 study on National Surgical Quality Improvement Program beyond logistic regression model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the advancement of the revolutionary artificial intelligence (AI) technologies, health-care services are rapidly moving toward an intelligent cyber physical system referred to as Health 4.0. In essence, the ability to predict surgical complications is all-important for both surgeons and patients. Recently, the use of machine learning (ML) algorithms for predicting complications has gained much attention. Even though many mature and reliable algorithms exist in the field of ML, the logistic regression (LR) algorithm has been the most widely used in complication prediction. In this study, we used the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database to compare the performance of LR to other ML algorithms for predicting complications during spine surgery. The database included 177 681 patients who underwent spine surgery. The occurrence of intraoperative morbidity was relatively low (9.4 per cent) in comparison to the total number of the dataset population, and hence, the dataset under study was considered imbalanced. To thoroughly evaluate and compare the proposed ML algorithms, the dataset was balanced and the algorithms were applied on both the balanced and imbalanced dataset. The results indicated that, in general, no significant difference was found between the performance of LR and random forest (RF), boosted tree (BT), and decision tree (DT).
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it