FRACTIONAL FLOW RESERVE: AN EXPERIENCE OF 100 PATIENTS AT AFIC-NIHD
Bibliographic record
Abstract
Objective: To evaluate the importance of Fractional Flow Reserve (FFR) in decision making in coronary revascularization in moderate lesions. Methodology: A retrospective descriptive study was conducted at Armed Forces Institute of Cardiology (AFIC) /National Institute of Heart Diseases (NIHD) from June 2008 to December 2012.A total of 100 consecutive patients who underwent FFR were assessed. These were the cases in which decision regarding percutaneous coronary intervention (PCI) was difficult on visual assessment alone. A 0.014” FFR wire was used and pressure gradients across the lesions were noted. Post procedural follow up was done at six months telephonically for symptoms of angina and heart failure and further treatment was planned accordingly. Results: A total of 100 patients whose coronary artery lesions were assessed by FFR were analyzed. The mean age was 54.5±8.9 years. Male patients were 89 (89%). The mean FFR score obtained was 0.84. In 25% of patients (n= 25) the coronary stenosis was found to be clinical significant (FFR 0.80). Based on the above results revascularization was done in 25 patients (21 PCI with stenting and 4 with coronary artery bypass graft surgery). Medical treatment was advised in 75 patients with FFR > 0.80. Only one patient in the >0.80 FFR group required stenting during follow up because of progression of disease and the rest were stable on medical treatment. Conclusion: FFR is important tool in guiding PCI in moderate lesions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".