The Ability of Heart Failure Specialists to Accurately Predict NT-proBNP Levels Based on Clinical Assessment and a Previous NT-proBNP Measurement
Bibliographic record
Abstract
BACKGROUND: The value of routine aminoterminal pro type B natriuretic peptide (NT-proBNP) measurements in outpatient clinics remains unknown. OBJECTIVES: We sought to determine the accuracy with which heart failure (HF) specialists can predict NT-proBNP levels in HF outpatients based on clinical assessment. METHODS: We prospectively studied 160 consecutive HF patients followed in an outpatient multidisciplinary HF clinic. During a regular office visit, HF specialists were asked to estimate a patient's current NT-proBNP level based upon their clinical assessment and all available information from their chart, including a previous NT-proBNP level (if available). NT-proBNP estimations were grouped into prognostic categories (<125, 125-1000, 1000-4998, or >/=4999 pg/mL) and comparisons made between actual and estimate values. RESULTS: Overall, HF specialists estimated 67.5% of NT-proBNP levels correctly. After adjusting for clinical characteristics, knowledge of a prior NT-proBNP measurement was the only significant predictor of estimation accuracy (p=0.01). Compared to patients with a prior NT-proBNP level <125 pg/mL, physicians were 95% less likely to get a correct estimation in patients with the highest prior NT-proBNP level (>/=4999 pg/mL). CONCLUSION: HF specialists are reasonably accurate at estimating current NT-proBNP levels based upon clinical assessment and a previous NT-proBNP level, if those levels were < 4999 pg/mL. Likely, initial but not routine NT-proBNP measurements are useful in outpatient HF clinics.
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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 | 0.020 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".