Bibliographic record
Abstract
We live in the information age, and the practice of medicine is becoming increasingly specialized. In the biomedical literature, the number of published studies has dramatically increased: There are now more than 15 million citations in MEDLINE, with 10,000 to 20,000 new citations added each week (1). Multiple relevant studies usually guide most clinical decisions. These studies often vary in their design; methodologic quality; population studied; and the intervention, test, or condition considered. Because even highly cited trials may be challenged or refuted over time (2), clinical decision-making requires ongoing reconciliation of studies that provide different answers to the same question. Both clinicians and researchers can also benefit from a summary of where uncertainty remains. Because it is often impractical for readers to track down and review all of the primary studies (3), review articles are an important source of summarized evidence on a particular topic (4). Review articles have traditionally taken the form of a narrative review, whereby a content expert writes about a particular field, condition, or treatment (5–7). Narrative reviews have many benefits, including a broad overview of relevant information tempered by years of practical knowledge from an experienced author. Indeed, this article itself is in a narrative format, from authors who have published a number of meta-analyses in previous years. In some circumstances, a reader wants to become very knowledgeable about specific details of a topic and wants some assurance that the information presented is both comprehensive and unbiased. A narrative review typically uses an implicit process to compile evidence to support the statements being made. The reader often cannot tell which recommendations were based on the author's clinical experience, the breadth to which available literature was identified and compiled, and the reasons that some studies were given more emphasis than others. It …
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 | 0.107 | 0.286 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.023 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".