Teaching Evidence-Based Complementary and Alternative Medicine: 2. A Conceptual Approach to Causation—Part 1
Bibliographic record
Abstract
One of the most common questions that arise in clinical practice is whether a causal relationship exists between two factors. In order to answer this question three steps need to be taken: First an association needs to be demonstrated between treatment/exposure and effect. Next, the possibility of this association being the result of error needs to be determined. Finally additional evidence to support a cause-and-effect relationship needs to be identified. Part 1 of this two-part paper describes how a complementary and alternative medicine provider goes through the first two steps when confronted with the question of whether silicone breast implants cause arthritis to develop. Also covered are the need for a control group when attempting to establish whether an association exists, the potential for systematic error (bias) or unsystematic error (chance) to distort an association, and the susceptibility of different study designs to systematic error.
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 | 0.037 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".