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Record W2313394118 · doi:10.1097/acm.0b013e31825371d0

If You Can’t Handle Death ...

2012· article· en· W2313394118 on OpenAlexaff
Brandy Stauffer, Jonathan White

Bibliographic record

VenueAcademic Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChoseWitnessMedicineFamily medicineLaw

Abstract

fetched live from OpenAlex

As doctors, none of us chose medicine because we enjoy dealing with human mortality. We chose medicine to be healers, comforters, figures of hope; we chose medicine because we want to save lives. At the start of my medical school career, I was not naïve enough to imagine that I would never be the bearer of bad news, never witness pain, chronic illness, or death. However, I never anticipated that death would become a daily part of my life or that we, as physicians, would be so incredibly undertrained in the frequent challenge of dealing with the frailty, morbidity, and mortality of life. I clearly remember the first time that one of my patients passed away. It was my first rotation of third year during my first shift in surgery. I had grabbed my resident and run upstairs to address a case of unexpected acute respiratory failure. Once there, we quickly realized that this patient would not make it through the night. He was a 92-year-old with terminal cancer, who had already been made a DNR. Although we ordered the standard labs and imaging, we knew the inevitable outcome; we called the family in, and, while waiting for them to arrive, he passed away. The senior resident and I were reviewing the chest X-ray and filling out the necessary paperwork when the family arrived, out of breath and disheveled as it was nearly 2:00 AM. This image is forever ingrained in my memory, and, even now, it causes me pain. Ironically, it is not the face of the patient, nor his death, that troubles me, but the faces of his family. While they had expected the eventual death of their father, they did not have the opportunity to say goodbye; he had passed away only minutes before their arrival. Later that same evening, the senior resident and I discussed his death with the junior resident. I was still visibly upset, and the junior resident noticed. He told me, “If you can’t handle death and you’re upset about stuff like this, then you should not be in medicine.” I fumed that if this was the junior resident’s callous approach to death, then he had no place in medicine. As physicians, we often fall short when it comes to handling death and dealing with its outcomes. Death is something that we haven’t been prepared for, something that we haven’t been taught, and something that, hopefully, few of us have had a lot of experience with. One would think that the longer you stayed in medicine, the easier dealing with death would get. I find that this is not the case. I never find death easy, and the faces of the patients whom I lose remain with me. I hope that I never stop remembering the families of my patients. These are the memories that remind me why we try so hard, why we do everything that we can, and why it is so important to take those few seconds to give patients and families a chance to say their goodbyes. In some cases, we will wish we had done things differently; in others, we will help families and patients toward a “good” death. In the end, at the end, what matters most are not the tests that were ordered, the surgeries that were performed, or any excuses for the circumstances, valid or invalid. Rather, it is the time that we spend with our patients and the small comfort that we can provide in those crucial moments. Death—whether imminent or likely—is one of the most terrifying moments for our patients and their families. When death beckons me, I hope that I will have a physician who remembers me, who spends those few extra moments with my family, helping them through one of life’s biggest challenges.

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)0.005
metaresearch head score (Gemma)0.042
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: Editorial · Consensus signal: none
Teacher disagreement score0.138
Threshold uncertainty score0.463

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.042
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.007
Scholarly communication0.0090.013
Open science0.0020.008
Research integrity0.0080.021
Insufficient payload (model declined to judge)0.1380.144

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 head0.053
GPT teacher head0.361
Teacher spread0.308 · 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
GenreEditorial

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
Published2012
Admission routes1
Has abstractyes

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