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Record W4206533729 · doi:10.1097/acm.0b013e31826cf08b

My Hands

2012· article· en· W4206533729 on OpenAlexaffabout
Mikayla Brenneis

Bibliographic record

VenueAcademic Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineGeneral surgeryStethoscopeSurgery

Abstract

fetched live from OpenAlex

Don’t touch that! What glove size are you? Watch your hands. My hands are well traveled by now—a year of clerkship puts a lot of experience in your hands. I’ve sutured lacerations on the belligerently inebriated. I’ve held the hands of grannies as they climbed onto the examination table. Many, many babies have gripped my index finger. I’ve palpated lymph nodes, cervixes, and prostates across the province. I know how to hold my stethoscope properly, like an internist. I don’t think I really thought about how much of medicine involves hands until my surgery rotation during clerkship. It seems obvious—tying, suturing, and cutting are clearly all about manual dexterity. They are skills that are taught and practiced, but it is more than that. During one of my days on thoracic surgery, we brought a young man into the operating room whose stomach contents were coming out of his chest tube. My job for the next four hours was to retract his heart for the surgeons. The flexible strength and elasticity of this organ was palpable through the metal retractor I held, and the patient’s life beat through my body. It was an incredible moment. Never had life been so clear to me as when the surgeon took a break and put my hand on our patient’s pericardium. I felt my pulse and his heart. Our rhythms were not in sync but we were the same—we were both alive. It was a grueling surgery, but it was amazing. The surgeon fixed the esophagus and stomach, and the patient lived. During one of my nights on general surgery, we brought another young man into the operating room. He had been absolutely fine three weeks ago, when he suddenly couldn’t keep anything down. The small bowel obstruction we saw on X-ray became something even worse on CT—cancer, likely pancreatic. No warning of painless jaundice. No weight loss. Maybe some night sweats. My job for the next four hours was to retract part of the intestine while the surgeon did a roux-en-Y gastric bypass to buy this gentleman some time and some quality of life. Partway through, the surgeon placed my hand on our patient’s intestine. It was hard and lumpy. The texture was all wrong. He let me run my hands along the length of our patient’s bowel with him—parts of it felt grainy and rough. We spent most of the operation in silence. It was cold and late. And we weren’t going to save him—help him but not save him. In surgery, my hands held life and death. I’ve held the strange weight of a leg as you hand it over to the nurse during an amputation. I’ve felt the unbelievable stretch of a skin graft as the surgeon staples it over a massive burn and the rush of air around my index finger as I clear the opening for a chest tube. I’ve understood the sorrow of the abnormal strength in a young trauma patient’s grip who has just earned the label of C6 quad. I’ve given my pen to the attending to sign a death certificate. My hands have changed and so have I along with them. I know who I am as a person, and I’m finding out who I might be as a physician, with a growing confidence in my abilities. My hands are the entryway, from the very first handshake with a patient, and they are one of the tools I am learning to use to connect with and care for my patients. My hands are a part of me and of my presence in medicine. Scrub in. Can you feel that? Hold this for me. Acknowledgments: A sincere thank you to Dr. Jonathan White of the University of Alberta Faculty of Medicine and Dentistry for encouraging the practice of reflection during the surgical clerkship rotation.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.468
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0100.008
Open science0.0020.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.4680.381

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 head0.076
GPT teacher head0.389
Teacher spread0.313 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2012
Admission routes2
Has abstractyes

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