Bibliographic record
Abstract
Abstract Clinicians need accurate and up-to-date information about emerging knowledge on assessment and treatment as well as other developments in practice. The presentation of this knowledge needs to be timely, accurate, and unbiased. In an ideal world, every psychiatrist would have instantaneous access to the original scientific articles. As this is not feasible because clinicians are busy and the skills needed for an adequate systematic search, critical appraisal, and interpretation of research articles are not routinely available. Further, the volume of research articles is staggering: about 2 million papers are published in 20 000 biomedical journals every year, and even if a psychiatrist restricted her reading to those clinical psychiatry journals it would be necessary to read about 5500 papers each year—equivalent to 15 papers every day. Clearly, a strategy is required for efficient and timely identification of research that is both methodologically sound and clinically relevant. A coherent set of strategies designed as a clinical tool to link the best available evidence directly to the care of individual patients was first formulated at McMaster University in Canada—an approach called evidence-based medicine. Evidence-based medicine is problem-based and splits the process of linking research to practice into five stages (formulating a structured clinical question, finding evidence and advances in the organization of clinical knowledge, using a systematic approach to searching for the best available evidence, applying the evidence to the clinical problem, and assessing and improving the process) plus the identification of clinical questions in need of more research. To make evidence-based practice feasible in real-life clinical practice, a number of problems need to be solved at each stage of the process.
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 | 0.085 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.031 | 0.026 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.014 | 0.030 |
| Insufficient payload (model declined to judge) | 0.036 | 0.017 |
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".