Bibliographic record
Abstract
To the Editor: “That looks like a solid deep dermal. Keep going!” the otolaryngology resident exclaimed as we were closing a parotidectomy incision on our first case together. As my needle looped through the patient’s skin, deep to superficial and back again on the other side, I reminisced about a day 8 months before. That day, when the general surgery resident handed me a needle holder with a Vicryl 4-0 suture and Adson forceps to close a laparoscopic incision, I buzzed with excitement. It was an uncommon opportunity for a clinical clerk rotating in a busy academic hospital to suture a patient. As I stared at the wound, I quickly replayed the sequence in my head: needle cutting into the skin at 90 degrees, equal bites on both sides, 2 turns in the same way and pull, repeat in the opposite direction with only 1 turn. I was visibly shaking as I attempted a first bite into the furthest border from me. “Too close to the opening,” I thought nervously as the tip of the needle peeked through the gap. Do I back up and try again? I quickly glanced up to check if anyone saw my mistake, only to find the resident working on another incision. Perhaps I could fix this if I took the bite again. “Why is the skin so tough to pierce?” I pondered as I attempted another try, oblivious that I had crushed the needle tip while grasping it. My struggle ended when the resident’s eyes landed on the rippled skin that my forceps were pulling and she completed my interrupted suture. “Don’t mind it,” the resident said. Except that I did. I recalled all the nights I had spent mirroring YouTube tutorials on my silicon skin model with expired sutures, the bottle handles full of shoe-lace square knots, and the blisters on my fingers from my incorrect needle holder handling. “Needle back,” I said as I gave my tools to the scrub nurse. Disappointment was an understatement of what I felt as I removed my scrub gown and exited the room that day. Returning from my retrospection, I heard the otolaryngology resident say, “That is an excellent subcuticular. I would not have done any better myself,” as I finished the final layer of suture. I smiled and thanked her for the compliment. “Needle back,” I announced, and the instruments left my hands. Xiya Ma, MScMD–MSc student, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada; [email protected]; Twitter: @ma_xiya.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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; both teacher heads 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".