Users' Guides to the Medical Literature
Bibliographic record
Abstract
Clinicians rely on knowledge about the clinical manifestations of disease to make clinical diagnoses. Before using research on the frequency of clinical features found in patients with a disease, clinicians should appraise the evidence for its validity, results, and applicability. For validity, 4 issues are important-how the diagnoses were verified, how the study sample relates to all patients with the disease, how the clinical findings were sought, and how the clinical findings were characterized. Ideally, investigators will verify the presence of disease in study patients using credible criteria that are independent of the clinical manifestations under study. Also, ideally the study patients will represent the full spectrum of the disease, undergo a thorough and consistent search for clinical findings, and these findings will be well characterized in nature and timing. The main results of these studies are expressed as the number and percentages of patients with each manifestation. Confidence intervals can describe the precision of these frequencies. Most clinical findings occur with only intermediate frequency, and since these frequencies are equivalent to diagnostic sensitivities, this means that the absence of a single finding is rarely powerful enough to exclude the disease. Before acting on the evidence, clinicians should consider whether it applies to their own patients and whether it has been superseded by new developments. Detailed knowledge of the clinical manifestations of disease should increase clinicians' ability to raise diagnostic hypotheses, select differential diagnoses, and verify final diagnoses. JAMA. 2000;284:869-875
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.015 | 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; both teacher heads 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".