Advices On Search And Critical Appraisal Of Biomedical Literature Part I, General Workflow
Bibliographic record
Abstract
Both physician-scientists and practicing physicians regularly experience the need to browse, select, and critically appraise the current biomedical literature. There have been many useful reviews on each of the aforementioned topics. Still, having a unified and coherent workflow, feasible to fit into a busy clinical routine, would surely be appreciated by physicians. In addition, many of the aforementioned reviews have been written to target the audience in developed countries. In contrast, particularities of the access to the literature in developing countries (or economies in transition) have been addressed less frequently. Finally, new computational approaches, including machine translation and automated text mining, are rapidly emerging. These are indeed worthy addressing as the initiatives that could provide a great help to practicing physicians for rapid, yet comprehensive literature appraisals.The present review aims to provide physicians with the workflow and methodological recommen- dation on browsing, selecting, and critical appraisal of the biomedical literature, with the specific focus on patient- and disease-oriented publications.The review further aims to overcome the aforementioned limitations of the previously published literature on this subject.
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 | 0.178 | 0.451 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.054 | 0.042 |
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".