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Record W4402937855 · doi:10.5489/cuaj.8996

Cancel half of your clinic visits* (a Halloween hot take)

2024· editorial· en· W4402937855 on OpenAlexvenueno aff
Michael Leveridge

Bibliographic record

VenueCanadian Urological Association Journal · 2024
Typeeditorial
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

Cancel half of your clinic visits * (a Halloween hot take) C arl alights from town plenty early, headed to see you.He had a partial nephrectomy for a grade 1 clear-cell six years ago and an ultrasound four weeks ago.He's retired, so doesn't have to fuss missing work, but on his fixed income, the 90 km round trip and parking make a dent.The office is characteristically bustling, and when you make it in to see Carl, your rump barely ricochets off the chair as you say, "Hey Carl, good news!The scan looks clear, no signs of new or spread tumor.Let's do it again in a year."Off he goes.Parking lot, Costco, home.Your clinical sense tells you that the imaging was necessary, but did Carl really need to come all this way for that?Did he need you at all?Recently I riffed on the inefficiency of most of our clinical interactions, but this month, I'll blast right past that with a modest proposal: most clinic appointments don't need to happen. 1 They could be replaced with a " " on a messaging service, a 90-second e-consult, or nothing at all.We function in a public-payer healthcare system that ought to be accountable for its limited resources.This system intends to provide the widest breadth of quality care as possible within that envelope.It is also important to note that while this system writes the checks, it is not specifically concerned with any clinician's income or lifestyle.Refer back here if you become appalled as you read: the pool is finite and unnecessary care may deny necessary care elsewhere.Back to Carl -he needs to know that his scan is fine, or not, or otherwise actionable.Surely, however, he doesn't always need to hear it in person, nor ought the system bear the full cost of overhead and remuneration attributed to such a fleeting exchange of month-old news.Same for a host of other visits.Referrals for simple cysts; incidental 3 mm calcifications on ultrasound; untreated LUTS; three UTIs in 18 months; bedwetting at age nine; a 10º penile curvature; a single PSA of 4.4 in a 72-year-old; a few disquieting hot pees; incidental microlithiaisis,

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)0.007
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: Editorial · Consensus signal: none
Teacher disagreement score0.804
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0050.001
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.8040.544

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 head0.067
GPT teacher head0.410
Teacher spread0.343 · 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
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

Citations1
Published2024
Admission routes1
Has abstractyes

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