The San Francisco Syncope Rule was useful for stratifying risk in emergency department patients with syncope
Bibliographic record
Abstract
Quinn J, McDermott D, Stiell I, et al . Prospective validation of the San Francisco Syncope Rule to predict patients with serious outcomes. Ann Emerg Med 2006;47:448–54. [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q In patients presenting at the emergency department (ED) with syncope, how well does the San Francisco Syncope Rule predict whether patients will develop a serious short term outcome not identified at the initial evaluation? Clinical impact ratings GP/FP/Primary care ★★★★★★☆ IM/Ambulatory care ★★★★★★★ Cardiology ★★★★★★☆ ### ![Graphic][5]</img>Design: prospective validation of a previously derived prediction rule. ### ![Graphic][6]</img>Setting: a university teaching hospital in San Francisco, California, USA. ### ![Graphic][7]</img>Patients: 760 patients 6–99 years of age (mean age 61 y, 54% women) attending the ED (791 visits) for syncope, defined as “transient loss of consciousness with return to baseline neurologic function,” or near syncope. Patients with loss of consciousness related to trauma, alcohol, drug use, or seizure were excluded. 54 patients with a serious outcome identified at the initial ED visit and 24 visits without the rule prospectively completed were omitted, leaving 713 visits … [1]: {openurl}?query=rft.jtitle%253DAnnals%2Bof%2Bemergency%2Bmedicine%26rft.stitle%253DAnn%2BEmerg%2BMed%26rft.aulast%253DQuinn%26rft.auinit1%253DJ.%26rft.volume%253D47%26rft.issue%253D5%26rft.spage%253D448%26rft.epage%253D454%26rft.atitle%253DProspective%2Bvalidation%2Bof%2Bthe%2BSan%2BFrancisco%2BSyncope%2BRule%2Bto%2Bpredict%2Bpatients%2Bwith%2Bserious%2Boutcomes.%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.annemergmed.2005.11.019%26rft_id%253Dinfo%253Apmid%252F16631985%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1016/j.annemergmed.2005.11.019&link_type=DOI [3]: /lookup/external-ref?access_num=16631985&link_type=MED&atom=%2Febmed%2F11%2F6%2F186.atom [4]: /lookup/external-ref?access_num=000237162900013&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".