Bibliographic record
Abstract
In performing a blepharoplasty, great care is always taken to precisely mark the exact incision line. Different techniques are described for this, but everyone has a system that is individually comfortable for them, and variable to the patient’s problem. Translating this careful ink mark into an incision is an additional challenge that we all face. Eyelid skin is exceedingly mobile, and redundant inelastic skin is even more mobile, requiring special efforts for counter-traction when incising with a blade. Slight overdistention of the skin with local anaesthetic is helpful, but the most effective means of countertraction I have found is the simple secretary’s rubber finger cot. This finger cot embodies the right texture with its fine rubber knobs, so that it has a high degree of friction to prevent the skin from sliding (Figure 1). That makes it superior to the standard operating room gloves for countertraction. Figure 1) Using a secretary’s finger cot to apply countertraction These devices autoclave perfectly well and are reusable. They offer a significant improvement in control for accomplishing the precise incisions required in blepharoplasty. They can also be used to atraumatically handle large slippery skin flaps.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".