Bibliographic record
Abstract
The rapid pace of knowledge accumulation in the field of kidney transplantation rendered traditional print textbooks irrelevant for most of my clinical training. The Kidney Transplant iBook by Leonardo V. Riella offers several advantages over traditional textbooks including the ability to update content (there have been four updates since its initial launch in May 2015) and to access content on portable electronic devices. Nevertheless, what really sets this book apart is the interactive format. Unlike electronic books in portable document format (i.e. PDF), the interactive iBooks format on Apple devices (Apple Inc., Cupertino, CA) allows the reader to zoom in on figures, access videos, complete interactive questions, link to online blogs and hyperlink to references. These unique features make the content accessible and enjoyable for readers (with the caveat that the book is accessible only on Apple devices, as the Kidney Transplant eBook). The content is designed primarily for students and trainees but also will be useful for the nontransplant clinician. The book covers familiar territory with topics including basic transplant immunology and immunosuppression, etiologies of allograft dysfunction and medical complications of kidney transplantation. Each chapter opens with a text box containing key learning points. Virtually every page contains a high-quality original or published image, photograph or graphic to facilitate learning; in some cases, these images may even be useful in explaining issues to patients. Multiple-choice questions at the end of each chapter reinforce the material; however, although the questions are interactive (readers receive immediate feedback on whether they have answered correctly), the rationale for the correct answer is not provided. The book also covers nontraditional topics including ultrasound, histocompatibility methods and test interpretation. It concludes with a series of case studies (including management of the pregnant patient and the patient with a failing allograft) that should prove highly useful for students and trainees. The depth of coverage will be insufficient for expert readers, and the book generally does not include in-depth analyses of controversial topics or areas of uncertainty. The author, however, does not shy away from providing recommendations in areas in which data are lacking. In some cases, these recommendations could be more clearly identified as opinion based on or reflective of practice at the Brigham and Women’s Hospital (BWH), where the author practices. Although Riella is the primary author, faculty members at BWH and Massachusetts General Hospital reviewed the content, and this is evident in the depth of coverage of certain topics. Information regarding drug dosing and drug interactions, for example, is particularly strong due to the involvement of Steven Gabardi, a doctor of pharmacy at BWH. Future iterations of the book would likely be strengthened with the involvement of additional contributors. In summary, the Kidney Transplant iBook is an innovative platform that likely marks the beginning of the end of the traditional textbook in kidney transplantation. I have no hesitation in recommending it for students, trainees and clinicians who want a basic overview of contemporary clinical kidney transplantation. The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.400 | 0.255 |
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".