Book Review of Clinics in Plastic Surgery: Local Anesthetics for Plastic Surgery
Bibliographic record
Abstract
This book continues the “spirit” of the Clinics update series in plastic surgery and is similarly formatted and presented. It is edited by Dr Huq from Canada and his mostly Canadian team. The book contains 14 chapters and has supplementary videos, although it was unclear just from this issue how they can be accessed. Dr Huq has an established university-based and private office–based practice in Ontario, and regularly performs procedures under local anesthetic (LA). Increasingly, patients are cost-conscious and expect minimal downtime. This has resulted in more requests for procedures under LA. This book has a wide-ranging and experienced faculty, and covers the important topics of LA related to plastic and cosmetic surgery. “A Primer on Local Anesthetics for Plastic Surgery” reviews the basic pharmacology of LA as well as new developments in its field, such as ultra-long-lasting LA. Following this, a chapter on cost analysis looks at potential cost savings in performing procedures under LA. However, it is also a reminder of increased staffing costs and patients’ attitudes regarding office-based cases. This is one of my favorite chapters; it addresses cost analysis, cost-benefit analysis, cost-effectiveness analysis, and cost-utility analysis. The meaning of these and their implications are important for all plastic surgeons to be aware of, particularly in the era of increasing outcome measures. This chapter emphasizes the lack of data on economic analysis in plastic surgery, which is also seen in anesthesiology. It also advises caution in assuming that it is better to perform procedures under LA in an office versus a cheaper, well-run public hospital, which “may have a lower indirect per-case cost allocation than may a high-end private hospital or aesthetic institute.” It also argues about medicolegal implications; my favorite sentence is this: “CEOs do not do their own faxing because their efforts are best put toward running the organization, and they are likely less efficient than their administrative assistants at faxing. To this end, one could argue that surgeons should operate and anaesthesiologists should anesthetize.”
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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.098 | 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".