Bibliographic record
Abstract
have reported that they have no relationships relevant to the content of this paperConclusions The lack of accessibility of quinidine is a serious medical hazard at the global level. (J Am Coll Cardiol 2013;61: 2383–7) ª 2013 by the American College of Cardiology Foundation E-mail received on June 8, 2012 (no editing was done): Hi, I hope this finds you well! I am sorry to trouble you, but I have no one else to ask! I have a patient who had an out-of-hospital cardiac arrest a couple of years ago, and had a defibrillator implanted. He returned last weekwith multiple shocks for VF, and a suspicious ECG for Brugada syndrome. He did not respond to beta-blocker therapy and I wanted to start quinidine for him- I recall having a few patients at Sunnybrook as a resident who were taking quinidine, and we ordered it without a problem. I tried to get quini-dine for this patient via SAPwith Health Canada, and it was declined- they stated they did not provide it for NEW patients, only patients already on the therapy. How are you managing your Brugada patients with multiple shocks or recurrent arrhythmias? This seems completely ridiculous! With kind regards,
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.743 | 0.693 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".