Performance of FAINT score for predicting poor clinical outcome in elderly patients presenting with syncope
Bibliographic record
Abstract
Abstract Background and Objectives Our study aimed to investigate the diagnostic accuracy of the FAINT score in predicting 30-day all-cause death and serious cardiac outcomes in patients aged 60 years and older presenting with syncope. Methods Our study, which was designed as a single-center, prospective cohort study, included patients aged 60 years and older who presented to the emergency department with complaints of syncope or presyncope. The primary outcome of the study was defined as 30-day all-cause death or serious cardiac outcome (poor clinical outcome). physician gestalt. Results Of the 172 patients included in our study, 9 patients (5.2%) were in the poor clinical outcome group, while 163 (94.8%) patients were in the good clinical outcome group. The sensitivity of the FAINT score was 77.8%, and the specificity was 33.7%. The sensitivity and specificity of the Canadian Syncope Risk Score, which showed the best diagnostic test performance, were calculated as 88.9% and 35.6%, while the sensitivity and specificity of the San Francisco Syncope Rule were 66.7% and 49.1%. The clinician's gestalt had a sensitivity of 33.3% and specificity of 97.6%, showing the lowest performance of all scorings. Conclusion The FAINT score showed lower success compared to the diagnostic test performance measures reported in the original study. According to the results of our study, we think that none of the scorings performed adequately and that there is a need to develop clinical decision-making algorithms with higher diagnostic accuracy in the management of patients presenting with syncope.
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 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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".