Searching the Literature: A Simple Step-Wise Process for Evidence-Based Medicine
Bibliographic record
Abstract
Sifting and sorting through the literature and research on health care is an important skill for practicing pharmacists. It is vital for staying current and, most important, helping with the critical task of avoiding adverse drug events in the optimal care of patients. Today, searching this literature efficiently and effectively is increasingly difficult at a time when clinical knowledge is growing exponentially. This article aims to provide a systematic process for going through the literature in an evidence-based manner.
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 | 0.147 | 0.259 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.052 | 0.024 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.027 | 0.018 |
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".