Conceptualizing the evidence pyramid for use in clinical practice: a narrative literature review.
Bibliographic record
Abstract
Objective: To explore contemporary iterations of the evidence pyramid as applied in evidence-based practice. Methods: We searched for articles published in PubMed, Web of Science, and Scopus databases between 2016 and 2024 that assessed the evidence pyramid and its application in clinical practice. Title/abstract and full-text screening were conducted by one reviewer to determine eligibility, followed by data extraction and analysis to summarize themes. Results: Of 83 full-text articles identified, 28 were included. Extracted information centred on three common themes: (1) use of the evidence pyramid as a guide, not a rigid tool; 2) importance of the clinical question; and (3) necessity of clinical expertise to integrate research findings into clinical decision-making. Conclusion: Preliminary findings of our review suggest that, when applying the evidence pyramid in practice, clinicians should consider context (i.e., the clinical question, best available evidence, patient preferences, and clinical circumstances), to optimize clinical decision-making and patient outcomes. Author’s Note: This paper is one of seven in a series exploring contemporary perspectives on the application of the evidence-based framework in chiropractic care. The Evidence Based Chiropractic Care (EBCC) initiative aims to support chiropractors in their delivery of optimal patient-centred care. We encourage readers to review all papers in the series.
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 | 0.119 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".