Bibliographic record
Abstract
Desorption electrospray ionization mass spectrometry (DESI-MS) allows for hyperspectral analysis of tissue samples non-destructively under ambient conditions. A downside of such a bountiful source of data is that the high dimensional images suffer from the curse of dimensionality, often requiring substantial pre-processing and complex algorithmic approaches to extract meaningful interpretations of the data. Postoperative atrial fibrillation (POAF) is a cardiac arrhythmia that can occur after heart surgery, resulting in poorer recovery and higher treatment costs. We proposed a stacked learning system using a combination of DESI-MS and patient demographic information to classify POAF in a set of patients. This stacked learning system was composed of three stages. First, a CNN was trained to identify nuclear hypertrophy in DESI-MS images. Second, the CNN’s output was combined with patient demographic information and dimensionally reduced. Finally, a support vector machine was used to classify POAF on the resulting dataset. Our study showed that the full stacked learning system outperformed the individual components and also provided a preliminary step toward predicting POAF in patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".