Trials and Tribulations of Evidence-Based Medicine: The Case of Alzheimer Disease Therapeutics
Bibliographic record
Abstract
The practice of EBM has evolved since it was first defined. Even the most ardent EBM proponents have recognized the need to modify the EBM approach and now include elements such as the patient's clinical state and circumstances, the patient's preferences and actions, and the physician's clinical expertise, as well as the research evidence. Part of the clinical expertise required in this model is the ability to interpret and apply the research evidence. This editorial has highlighted some of the challenges faced by clinicians who would treat AD with an EBM approach. It suggests that, without a sophisticated understanding of the primary research studies, it might be hazardous to rely on a single RCT, or even on a systematic review, for treatment recommendations. If expert assistance is required, one solution might be to rely on clinical practice guidelines developed through a consensus approach. Although clinical practice guidelines have also been criticized, they appear to provide the best blend of EBM and expert opinion. The results of the Third Canadian Consensus Conference on the Diagnosis and Treatment of Dementia will be published shortly. Hopefully, this will provide helpful guidance for Canadian clinicians and their AD patients.
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 | 0.092 | 0.358 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.089 | 0.099 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".