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Record W2409404825

Room for a view : Specialists

2001· article· en· W2409404825 on OpenAlexvenueno aff
Samir Gupta

Bibliographic record

VenueCanadian Medical Association Journal · 2001
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsnot available
Fundersnot available
KeywordsStethoscopeJargonDutyMedicinePsychoanalysisPsychologyPhilosophyLinguisticsRadiology
DOInot available

Abstract

fetched live from OpenAlex

As a child, I remember watching intently my pediatrician's steel stethoscope as it swung back and forth on his neck, like a hypnotist's pendulum, lulling me into a near-panicky dread of its cold metallic shock on my skin. Then, just before he placed it on my chest — I would brace myself, every single time — he would miraculously remember to warm it up with his hands. And all fear would be forgotten. Nowadays, technology has “progressed”: we do not have those cold stethoscopes any more. Instead, we have an armamentarium of much colder and darker things, like MRI machines, bronchoscopes and MRSA masks. Modern textbooks talk about things like blood samples, CT scans and MRIs as being “more dependable than the physical exam,” but that's not the point. These tests are the idioms of a modern medical jargon that patients simply do not speak. Their language is the language of the physical exam, however pointless it may seem to us at times. In a strange metaphorical way, I feel that it is now my duty to warm up the stethoscope, somehow, through explanation and shared concern, to lessen the cold shock of the unnatural devices and procedures we now use to help our patients. The first step in achieving this is to understand that our notion of what constitutes caring for the patient does not necessarily (and probably does not usually) coincide with the patient's idea of what it is to be cared for. Recently, I went to see a specialist for a recurrent problem that I have had for as long as I can remember. Roughly, our interaction went as follows: after we introduced ourselves to one another, I candidly told him exactly what the problem was, detailing it as any self-respecting medical student would. He acknowledged the problem and proceeded to ask me exactly how I would like things to be: essentially, what I thought he could do for me. After this, he took a moment to consider the problem, comb through the details and cut to the heart of the matter. He posed a few more questions and pondered further. Next, he offered his expert opinion and treatment plan and asked if I understood and agreed with his strategy. Finally, he proceeded with an extensive examination and the first treatment. Before I knew it, conversation was flowing freely, taking root in the frivolous banalities of small talk and blooming — an hour later — into the sharing of views and goals and, indeed, the sharing of many personal stories, as between friends. The power differential between the expert and his subject, and the disempowering act of sharing a personal concern with a stranger and putting myself “in his hands” seemed much easier now that the expert was also a person. Before I left that day, I scheduled another haircut in six weeks and wondered, “Why can't doctors be like that?”

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.471
Threshold uncertainty score0.755

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0080.008
Open science0.0020.008
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.4710.203

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 head0.036
GPT teacher head0.382
Teacher spread0.346 · 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.

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
Published2001
Admission routes1
Has abstractyes

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