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Record W4244936578 · doi:10.1097/aln.0000000000003533

Stars

2020· article· en· W4244936578 on OpenAlexaff
Christoph N. Seubert

Bibliographic record

VenueAnesthesiology · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsAssembly of First Nations
Fundersnot available
KeywordsMedicineUnconscious mindAnxietyMindsetAnestheticAnesthesiaSurgeryPsychiatryPsychoanalysisPsychology

Abstract

fetched live from OpenAlex

AS an anesthesiologist administering a general anesthetic, I perform the ultimate example of episodic care. The encounter is focused around one episode, one specific procedure in a patient’s life. The patient is unconscious for most of the encounter, reduced to a set of parameters on a monitor and mostly hidden under surgical drapes, while human structures are exposed and worked on that normally don’t see the light of day. Thus, it is easy to slip into production-line mode: the preanesthetic evaluation done by one person, intraoperative anesthesia care by another, and recovery by another yet. Such a production mindset can make light of the fact that for most patients, having an operation is understandable—at least on some level—whereas being under general anesthesia is not. A frequent response to an upcoming surgical procedure is concern about specific complications, whereas a typical response to an upcoming anesthetic is anxiety.“Your next patient is really cute,” the preoperative nurse tells me. Cute is not the first word that comes to mind as I prepare to see a small-for-age, developmentally delayed, 12-year-old with cerebral palsy in need of a revision of a ventriculoperitoneal shunt. I enter the preoperative bay to find a beaming boy, quite obviously not cowed by the difficult hand life has dealt him, accompanied by his friendly father. I sit on the boy’s stretcher, a little closer than a stranger should—intentionally—not only to talk at eye level, but also to gauge the boy’s response to strangers, knowing that what comes next will invade his space much more. He looks at me, interested—briefly—only to return to playing on the tablet computer in his hand. I ask the father how the boy handles procedures, given that many preceded this one. He likes to sing “twinkle, twinkle, little star” as he goes back for a procedure, his dad shares.“Your next patient is really cute,” I tell the nurse anesthetist I’m working with. She is a reserved person with more than two decades of clinical experience, who does not suffer fools gladly. “Yes, he is,” says the young operating room nurse. We go to pick up our patient. After a “See you later” from dad, the nurse begins “twinkle, twinkle, little star” and our group finds its pitch as we sing and travel in the long hallways of our Neuromedicine Hospital. Patient support technicians, the essential low-wage workers who keep our operating rooms stocked and clean, turn their heads as we pass by. After the briefing in the operating room, the nurse anesthetist gently places a face mask providing nitrous oxide over the boy’s nose and mouth. Under the stress of imminent events, the boy starts singing yet again. We join as sevoflurane is added to the inspired gas mixture. “Twinkle, twinkle, little star,” we sing, “and you’re off to space,” the nurse anesthetist says, noting the arrival of unconsciousness, “where stars don’t twinkle,” I say, ever the nerd. Why is that? Lack of atmosphere for refracting starlight. At the conclusion of the operation, we return a sleepy but comfortable boy to his father in the recovery room. As I return to the operating room to face my next task of the day, a patient support technician calls to me: “Hey, doc, I didn’t know you could carry a tune…”The production mindset in health care, which pushes us toward a state of perpetual preoccupation, also generates slogans used to advertise our work. Our slogan in neuromedicine is to “strive for the perfect patient experience.” Day after day we try, typically only to fall short, time and time again, for a frustratingly diverse and very human set of reasons. Sometimes, though, we get close. Humans caring for fellow humans. The tension between the production pressures, our patient’s humanity, and the humanity of those delivering care, resolved. All stars.

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)0.002
metaresearch head score (Gemma)0.009
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: Other · Consensus signal: Other
Teacher disagreement score0.734
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0090.006
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.7340.687

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 head0.030
GPT teacher head0.263
Teacher spread0.233 · 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
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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