Bibliographic record
Abstract
Evidence Based Resource in Anaesthesia and Analgesia M.Tramer, ed. London: BMJ Books, 2000. ISBN 0-7279-1437-5. 225 pp. $30.00. Has it come to this? A review of reviews? All physicians understand the need for original study of important clinical questions, and appreciate the perspective offered by review articles about a subject. But do we really need a review of review articles? Unfortunately, randomized clinical trials ask “does intervention “X” work compared to placebo?” rather than “how well does intervention “X” work?” Narrative reviews add little but the personal perspective of the author, and are often biased in their sampling of the literature. And so we have progressed to systematic reviews that sample the literature objectively, assess the quality of studies (randomization, blinding, and measured attrition), and provide both qualitative and quantitative answers to medical questions. This book devotes itself to the application of “evidence-based medicine” to the specialty, explains what is involved in the systematic review process, and catalogues the currently published work of interest to anesthesiologists. The authors are well versed in epidemiology and are, fortunately, less than evangelical in their approach to the subject than some have been. They carefully point out that there is no evidence (nor likely to be) that “evidence-based medicine” provides better medical care than what we like to call that which went before. They also caution that large research projects or systematic reviews fail to hold direct relevance to clinical practice, and to medical decisions about individual patients. Finally, they point out that lack of evidence from high-quality studies does not constitute evidence of lack of effect, but only that we have a measured uncertainty in our data base. This book will be enjoyed by those who wish to reliably interpret the literature of anesthesia. It also provides a comprehensive list (to the end of 1999) of systematic reviews of interest to the specialty. Notably, only 51% were published in our specialty journals! It provides a lucid explanation of the terminology of “evidence based medicine”, and presents an approach to modern medical decision-making. For example, the three systematic reviews of the role of epidural analgesia in the genesis of Caesarean sections all came to different conclusions. Can they all be correct? Finally, as examples, the authors provide comprehensive discussions of the evidence on which anesthesiologists manage acute pain, treat nausea and vomiting, and reduce the need for allogeneic blood transfusion. That we find we don’t know as much as we thought should not reflect adversely upon anesthesia research, but only stimulate an open mind to new ideas as we elevate the “standard of proof.” If there is a deficiency in this book it is in the lack of justification of the principles of “evidence-based medicine” in a specialty such as anesthesia. Our “facilitating” medical specialty works within a complex medical system and may have different requirements for “evidence” than does a traditional specialty, charged as the latter is with responsibility to treat a specific disease entity. Can the principles of evidence be applicable if there is not a direct cause-and-effect relationship of the anesthesiologist’s action to the patient’s outcome? That reservation aside, the value of this monograph lies not in the cited evidence (which will soon become dated), but in the way it encourages one to think critically. It has come to this, and fortunately so!
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.061 | 0.050 |
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".