The MLD MAX OCT algorithm: An imaging‐based workflow for percutaneous coronary intervention
Bibliographic record
Abstract
Although extensive clinical data support the utility of intravascular imaging to guide and optimize outcomes following percutaneous coronary interventions (PCI), clinical adoption remains limited. One of the primary reasons for limited utilization may be a lack of standardization on how to best integrate the data provided by intravascular imaging practically. Optical coherence tomography (OCT) offers a high-resolution intravascular imaging modality with integrated software automation that allows for incorporation of OCT into the routine workflow of PCIs. We suggest use of an algorithm called MLD MAX to incorporate OCT for imaging-guided interventions: the baseline OCT imaging run is intended to facilitate procedural planning and strategizing, consisting of assessment for predominant lesion morphology (M), measurement for stent length (L) and determination of stent diameter (D); the post-PCI OCT imaging run is designated for assessment of need for further optimization of stent result, and consists of analysis for medial dissections (M), adequate stent apposition (A) and stent expansion (X). Incorporation of the MLD MAX algorithm into daily practice guides an efficient and easily-memorable workflow for optimized PCI procedures.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".