Bibliographic record
Abstract
DENNIS C. TURK and RONALD MELZACK (EDS.) Handbook of Pain Assessment, Second Edition New York: The Guilford Press, 2001, 784 pages (ISBN 1-57230-488-X, US$75, Hardcover) Reviewed by PATRICK MCGRATH The Handbook of Pain Assessment is a comprehensive review of the state of the art of pain assessment. The book consists of 36 chapters organized in six major sections, an introduction and a conclusion. The sections are: measurement of pain, assessment of behavioural expressions of pain, medical and physical evaluations, psychological evaluation, specified pain states, and methodological issues. The editors are distinguished psychologists who have made major contributions to the literature on pain. Turk and Melzack have chosen an impressive roster of contributors. Many are senior clinician scientists but there are a number of rising stars. Most are from the United States but about a fifth of the authors are from Canada. The rest of the world is not really represented. Most of the authors are psychologists but there is a smattering of other professions. I should alert the reader of this review that, although I did not write any of the chapters, nor have I collaborated with the editors, both editors and many of the authors are well known to me. A few are friends and collaborators. Several authors have cited my work. However, I think I can give a fair review of the book. The chapters are generally well written and they appear to have been well edited. I could find very few typographical errors and there is little redundancy across chapters. Although the chapters are quite scholarly and well referenced, there are many practical aspects. For example, there are dozens of tables and many figures. As well, many chapters include helpful appendices of specific assessment measures. The eminence of the editors, the quality of the authors, and the breadth of coverage will ensure that this volume is widely used. Each reader will have his or her own favourite chapters. The book is too long for me to review all the chapters that I thought were very good. However, I personally was delighted and challenged by the very detailed and scholarly chapter on psychophysical approaches. The discussion of affect in pain in this chapter is the most sensible I have seen. As well, I learned a lot from the chapter on neuropathic pain. The insights that I gained from this chapter will change what I do in the clinic. The chapters on the elderly and children were detailed and helpful. The chapter on pain assessment in the cognitively impaired was very welcome as this population has been so poorly assessed. …
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.058 | 0.051 |
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".