An Enhanced Patient-Specific Computer Model Using an Iterative Multi-Staged Algorithm
Bibliographic record
Abstract
Background: A previously introduced patient-specific computer model was applied to predict outcomes of the balloon occlusion test (BOT). The goal of this work was to propose an enhanced patient-specific computer model using a new iterative multi-staged algorithm. Method: This work presents an enhanced patient-specific computer model which converts several more terminals from generic, to patient-specific, resistances and also makes the aortic pressure and the stiffness coefficient patient-specific. A new iterative multi-staged algorithm is proposed to determine the terminal resistances, the aortic pressure, and the stiffness coefficient using vessel sizes and vessel flows measured by quantitative magnetic resonance angiography (qMRA) and cuff pressure. The predictions of the BOT were also evaluated for the validation of the proposed model. Results: The predictions from the proposed model were accurate in comparison with the clinical outcomes of the two BOTs. The computed stiffness coefficients were consistent with the ages of the two patients. The correlation coefficients between the flow measurements and the computed flows using the proposed computer model at baseline for the two patients were 0.9861 and 0.9995, respectively. The computed aortic pressures were also comparable in waveform shape with the actual aortic pressure measurements obtained during the BOT using a catheter-transducer. Conclusion: The present study makes the computer model more patient-specific with a new algorithm using non-invasive data from qMRA and cuff pressure. The six more sectors have made the terminal resistances more patient-specific. The patient-specific aortic pressure has made the forcing function patient-specific. And the stiffness coefficient has made the compliance patient-specific. J Neurol Res. 2017;7(3):25-38 doi: https://doi.org/10.14740/jnr435w
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".