Evidence-Based Medicine Applied to Fixed Prosthodontics
Bibliographic record
Abstract
People who seek help from professionals have a right to expect that formal measures have been taken to assess the relative merits of the various forms of health care on offer, be these, for example, radical surgery or fixed prosthodontics.1 There is increasingly wide support for the principle of reliable assessment of the effects of health and social interventions on outcomes that matter to the people to whom they are offered. Debate continues, however, about the methods of assessment that should be used in implementing this principle in practice. Different strategies for improving treatment effectiveness and quality have been proposed under different names. “Out-come research”, “technology assessment methodology”, “quality management and as-surance”, “clinical guidelines”, “parameters of care”, “health economy analyses”, etc. are familiar terms. Which strategy is selected and, perhaps more important, funded, is influenced by current beliefs and priorities in society. However, a common denomi-nator of the different strategies is the concern about the appropriateness of care, whether on an individual or on a population level. It is in this context that a new strat-egy for teaching the practice of medicine, named evidence-based medicine (EBM), was introduced in 1991 at the McMaster University in Canada.2 The rationale for changing the teaching strategy was the assumption that although traditional medical training resulted in a more-or-less thorough understanding of basic mechanisms of disease and pathophysiological principles, this combined with common sense and unsystematic observations from one’s own clinical experience did not prepare the physician for assessing and evaluating the new diagnostic tests, treat-chapter 10
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 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".