<i>Editorial Commentary</i> : Machine Learning and Artificial Intelligence Are Valuable Tools yet Dependent on the Data Input
Bibliographic record
Abstract
Machine learning is likely to become one of the most valuable tools for predicting outcomes in patients with patellofemoral instability. Traditional statistical analysis is challenging in this diagnosis as the result of the multitude of risk factors. However, 3 important cautions must be considered. (1) Machine learning is limited by the quality of the data entered. Many of the risk factors for patellofemoral instability rely on classification systems with significant interexaminer variability and patient-reported outcomes used to track changes contain inherent biases, especially with regard to race and gender. Poor data quality will lead to unreliable predictions, or "garbage in equals garbage out." (2) The optimal machine-learning algorithms for addressing specific clinical questions remain uncertain. (3) The question of how much data we really need for accurate analysis is unresolved, which again, is completely dependent on the quality of the data. Machine learning is the future; just beware of what goes into the chicken salad.
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 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 | 0.032 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".