Bibliographic record
Abstract
Drs Tenczer and Tomcsanyi draw further attention to the classification of atrial fibrillation (AF). Clarity, simplicity, and precision compete with one another in any classification scheme. Clarity and simplicity are major determinants of whether or not a classification scheme will be used. Precision minimizes ambiguity, contributing to utility, but can make a classification scheme cumbersome. These problems were recognized in devising the scheme for the classification of an episode of AF promoted in the treatment guidelines.1,2 The scheme is designed for classification of a single episode of AF and classification of a patient with AF is dealt with elsewhere.3 The current scheme has strength in its simplicity, utility at the bedside, theoretic and therapeutic implications. A weakness of the scheme is the arbitrariness of the time-base. Leaving aside new onset (first detected) and permanent (accepted) AF, there are essentially two forms of AF—paroxysmal and persistent. It is generally agreed that the distinction between these two is that paroxysmal AF is self-terminating within a short period of time, whereas persistent AF is long lasting, usually, but not always, terminated by cardioversion. An observation period must be specified for self-termination within a short period of time. In the current scheme the time limit is set at 7 days. Drs Tenczer and Tomcsyani, many others and even the Guidelines1 agree that what we mean by paroxysmal AF mostly terminates within a shorter period of time, usually 24–48 h. Of course, some AF terminates spontaneously after much longer periods of time. I have observed spontaneous termination of AF after more than 7 years. Is there practical value to further subdivide episodes of AF into that self-terminating in various time-based ‘bins’, as suggested by Drs Tenczer and Tomcsanyi? Such a proposal is more precise but has some drawbacks. It makes the scheme more complex. Terms like ‘acute’ and ‘chronic’ are inherently ambiguous. Its theoretic and/or practical importance is unknown.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.027 | 0.055 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".