When there is little or no research evidence: a clinical decision tool.
Bibliographic record
Abstract
Despite advancements in research and guidelines of healthcare, there are still situations where clinicians may lack experience or face limited evidence to inform decision-making. In these situations, healthcare providers should provide care within their scope of practice considering all available evidence-based options, the patient's preferences, and the clinical context through a clinical expertise lens. This decision-making tool serves as a guide for patient-centred clinical decision-making in chiropractic care. It integrates clinical expertise with the pillars of evidence-based practice, taking into account the best available research evidence, patient preferences, and the clinical context. Examples are provided on using the tool within chiropractic care for conditions with large bodies of supporting evidence (e.g., low back pain), and conditions with little to no evidence (e.g., Parkinson's disease), to illustrate the broad applicability of how to use (and how not to use) this tool in the field of chiropractic care. 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.
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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 | 0.314 | 0.563 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.035 | 0.017 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.038 | 0.042 |
| Open science | 0.010 | 0.022 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".