Bibliographic record
Abstract
Dr. Sarukawa and his colleagues are to be congratulated for bringing objectivity to our subjective clinical impressions in the area of skull-base surgery. They also articulate what many of us have been thinking, that our reconstructions are often not very good. The authors give us documented justification, for a change, in the way we approach complex craniofacial defects. Microvascular surgery has, to a large extent, been responsible for the advances that have taken place in skull-base surgery, allowing more extensive resections in the knowledge that the defect can be safely covered.[ 1 ] However, we have tended to concentrate on wound closure without paying much attention to facial aesthetics in these complex cases. The rectus abdominis myocutaneous flap has been the workhorse for these reconstructions, and we are all aware of the variable atrophy that occurs with this and other muscle flaps. We have all had patients who looked great after the initial surgery, only to witness the subsequent loss of bulk with consequent deterioration in aesthetics associated with flap atrophy. This paper attempts (and I think succeeds) to define the problem but, more important, gives us a method of objectively measuring our results. The concept of PSA and POA was, at first, difficult to grasp but it is an elegant one. The perfect reconstruction is when PSA and POA are 100 percent. That is our goal. While it is intuitive that reconstructing the bony defect will result in a better outcome, we can clearly see that in this series, the two patients who had bony reconstruction maintained their PSA and POA numbers with much less variation that those reconstructed with soft tissue alone (see Fig. 5). It is also intuitive that flaps supported by underlying bone are less likely to droop or at the very least, will droop less than those that are unsupported. Many of us have already started to move away from soft-tissue-only reconstructions for complex defects such as the maxillectomy defect,[ 2 ] and the results are gratifying. This paper shows us objectively that our clinical impressions are correct. There are still many challenges. The complication rate cited in this paper is in keeping with other reported series and underlines the complexity of these cases. This, at least in part, is why we have opted for flaps such as the rectus abdominis. It is a safe flap, relatively simple to harvest, with a large and reasonably long pedicle. While safety is important in everything we do, in this region it is particularly important because the consequences of failure are so serious. Furthermore, the region is one of the most anatomically complex, so that the defects that result from surgical ablation are three-dimensional and frequently incorporate multiple skin and mucosal surfaces, together with the varying convexities and concavities of the facial skeleton. Reconstructing all elements of this defect tests our reconstructive ingenuity. This paper shows us, in an objective way, what needs to be done. The challenge is now to devise ways of incorporating bony reconstruction in our treatment paradigm in the context of high stakes surgery, in which the clinical risks of exposure to the aerodigestive system, dural patches, and adjuvant treatment with chemotherapy and/or radiation all come into play. Any or all of these associated risks can have an adverse effect on ultimate outcome. Once again, the authors are to be commended for their objectivity.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.313 | 0.124 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".