Bibliographic record
Abstract
David L. Brown, Gregory H. Borschel, and Benjamin Levi, eds. Michigan Manual of Plastic Surgery , 2nd Edition. Philadelphia, PA: Lippincott Williams & Wilkins, 2014. ISBN-10: 1451183674, ISBN-13: 978-1451183672, $69.99. ![Graphic][1] It is with great pleasure that I provide a book review for the second edition of Michigan Manual of Plastic Surgery , edited by Drs David L. Brown, Gregory H. Borschel, and Benjamin Levi. The first edition was edited by Drs Brown and Borschel and released in 2004. I fondly recollect visiting the Section of Plastic Surgery at the University of Michigan at that time and receiving a copy of the first edition by from Drs Brown and Borschel—hot off the press. I have since read that first edition cover-to-cover several times. For this second edition, Dr Levi accompanies the original editors. The book keeps the same lightweight, pocketbook look and feel. While the first edition was printed in black and white, the second edition is in color and also includes some color figures. This edition sees an increase in chapters from 54 to 59 and is 670 pages in … Corresponding Author: Dr Jamil Ahmad, The Plastic Surgery Clinic, 1421 Hurontario Street, Mississauga, Ontario, Canada, L5G 3H5. E-mail: drahmad{at}theplasticsurgeryclinic.com [1]: /embed/inline-graphic-1.gif
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.350 | 0.228 |
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".