The Challenges in Presenting Complex Studies
Bibliographic record
Abstract
Sir: Dr. Friedman’s comments regarding our article highlight some of the challenges that are inherent in the presentation of certain elements of a somewhat abstract scientific subject to a readership composed largely of clinicians. Our objective in this article was to share our electron microscopic observations of periprosthetic breast capsules and to demonstrate that differences in clinical practice may lead to alterations in their structure. Needless to say, discussion and debate concerning articles that shed light on new aspects of the periprosthetic capsule are necessary and ensure that the body of knowledge in this emerging area of our field continues to grow, ultimately leading to improvements in the implant-based operations that we as plastic surgeons offer our patients. However, on carefully reviewing Dr. Friedman’s letter, we note that he compares the scanning electron microscope to the light microscope, which is by nature completely different.1 This inappropriate comparison is doubled by an apparent misunderstanding of the process involved in scanning electron microscopy.2 Light microscopy is the transmission of photons through the specimens and indeed requires a cell biologist or pathologist for accurate analysis. In contrast, scanning electron microscopy is performed by scanning of a fine electron beam over the sample and is performed by specialized technicians. Many members of our team use scanning electron microscopy on a regular basis and have published several articles on the breast capsule and other topics using this tool. With respect to the publication review process, many pertinent questions regarding scanning electron microscopy were addressed by our team before the article was accepted. The images were selected to accurately reflect the multiple sample analysis that was performed in this study while respecting the guidelines and limits of the Journal. Both images in the first figure were shot at the same magnification, but the right part is a subset of a larger image. We decided to show only the capsule interface, to remove information that was irrelevant to the basic research question (Fig. 1).3Fig. 1: First image presented in the article. Images were acquired from the scanning electron microscope. (Left) The upper part is the cross-section of capsule (arrowhead indicates a blood vessel) and is separated by a dashed line from the prosthesis interface on the lower part. (Right) Schematic depiction of sample orientation of the left image (rectangle indicates scanned field).In the fourth figure, we stated in the figure legend that both group 3 and group 4 displayed comparable capsular architecture measurements taken using computer software, as described in the Methods section, and were used to ascertain this. We did not feel that submitting the same image twice would have added any valuable scientific information to the reader. With regard to blinding, we did mention that all scanning electron microscopic sample analyses were performed in a blinded fashion. All patients were assigned an inclusion number. The specimens were transferred to the scanning electron microscopy laboratory labeled only by their respective number. Once analyzed, results were sent back to the first author for compilation with the clinical data. DISCLOSURE Dr. Danino is a consultant and speaker for Allergan Inc. None of the other authors has any commercial associations or financial interests to declare with respect to any of the information or products presented in this communication. Operational study costs were partially supported by an Allergan Inc. industry research grant. Jean-Philippe Giot, M.D., Ph.D. Laurence S. Paek, M.D. M. Alain Danino, M.D., Ph.D. Division of Plastic and Reconstructive Surgery University of Montreal Hospital Center Université de Montréal Montreal, Quebec, Canada
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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 | 0.160 | 0.501 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.016 | 0.040 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".