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Record W3007678642 · doi:10.1097/acm.0000000000003217

“Needle Back”: Remembering My First Suture

2020· letter· en· W3007678642 on OpenAlexaffabout
Xiya Ma

Bibliographic record

VenueAcademic Medicine · 2020
Typeletter
Languageen
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineFibrous jointSurgeryForcepsMistakeAnatomyGeneral surgeryLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.009
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.077
GPT teacher head0.307
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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