Invited Article: Practice parameters and technology assessments
Bibliographic record
Abstract
A neurologist is evaluating a patient in the office after the patient reported a 5-minute episode of blindness of the right eye and clumsiness of the left arm. She orders a carotid ultrasound that reveals some carotid plaques, with a <50% stenosis of the right and left carotid artery. She consults a vascular surgeon, who recommends immediate right carotid endarterectomy. Before she proceeds, she checks the American Academy of Neurology (AAN) Web site to see if there are any relevant practice parameters on carotid endarterectomy. She discovers that endarterectomy is not recommended for her patient.1 Armed with this definitive information, she discusses treatment options with the patient and the surgeon and prescribes antiplatelet therapy, as recommended in the guideline. In light of her recent positive experience with guidelines, she is quick to check the AAN Web site when seeing her next patient, a child who has a new diagnosis of absence seizures. Hoping to receive guidance in selecting the proper first therapy, she finds a guideline on treatment of the patient with newly diagnosed seizures.2 The only recommended therapy is lamotrigine (Level B), and she selects this treatment for her patient. The child continues to have seizures, so she asks an epileptologist for an opinion. The epileptologist questions why she did not use ethosuximide, which is considered first-line therapy for childhood absence epilepsy.3 She switches the child to ethosuximide, wondering where she and the guideline went wrong. These two scenarios illustrate the proper use and the limitations of guidelines. Guidelines are often misunderstood relative to their implications and purpose. As guidelines become an ever-increasing presence on the healthcare landscape, for neurologists as well as in other areas of medicine, it is important to explain what AAN guidelines are and what they are not. An AAN practice parameter …
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".