Kelley and Firestein’s Textbook of Rheumatology, 2-volume Set, 10th Edition
Bibliographic record
Abstract
Kelley and Firestein's Textbook of Rheumatology is a well-organized resource for residents and practicing rheumatologists.The first half of Volume 1 covers anatomy, immunology, as well as broad topics such as cancer risk in rheumatic diseases and pregnancy.As a trainee, I found it very helpful to have basic science concepts presented in a way that helps me understand their clinical relevance.This volume also reviews the approach to regional pain, laboratory testing, injection techniques, imaging, and pharmacology.The summary tables were very helpful to highlight key points.Volume 2 dives into the individual rheumatic diseases with excellent clinical images.Each chapter delves into the background, pathophysiology, outcome measures, and treatment with an extensive list of resources for additional reading.Given the extent of topics covered, I found the headings to be particularly effective to narrow down the areas I wanted to review or re-read.Kelley and Firestein's Textbook of Rheumatology is an excellent 2-volume series that covers the spectrum of rheumatology, from understanding the molecular basis of disease to approaches to practical topics (injection techniques, imaging) to specific disease entities, both common and uncommon.I would highly recommend this book for rheumatology residents as a study resource and practicing rheumatologists as a reference book given its thorough yet succinct delivery of many topics.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 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".