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Record W2426572089

Heart & Soul: From factory shipper to hospital cardiologist

2002· article· en· W2426572089 on OpenAlexvenueaboutno aff
Susan Pinker

Bibliographic record

VenueCanadian Medical Association Journal · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBattleOfficerMedicineMedical emergencyLawHistory
DOInot available

Abstract

fetched live from OpenAlex

When it comes to medicine, Dr. Sydney Segall takes the long view. The very long view. When the 85-year-old Montreal cardiologist started practising in 1950, there were no pacemakers, angiograms, defibrillators or cardiac care units. And MI patients were treated with 6 weeks of strict bed rest. “We do things differently now,” he says. Segall had his introduction to frontline practice in Normandy. As a medical officer who arrived in France right after D-Day, he witnessed first hand soldiers' shock, battle exhaustion and panic. “These were young kids who watched their friends getting blasted to bits. One sergeant had tremors and could barely talk, but insisted on going back to his unit. I had to decide whether to treat them or send them home. The majority I sent home.” Today, the condition would likely be diagnosed as post-traumatic stress disorder. In 1944, it was called battle fatigue. But while 5 intervening decades have brought dramatic changes to medicine, Segall sometimes gets a sense of deja vu. The waiting lists and bed shortages of 2002? In the '50s, says Segall, beds were tight and there were waiting lists from his first day in practice. The Jewish General Hospital in Montreal had 150 beds when he started practising there. It has 600 today, but the situation hasn't changed. “Even in 1950 it was very difficult to get a patient past the admissions officer.” Has medicare improved access to care? Perhaps, but Segall still remembers the cardiology clinics where patients got treated regardless of income, where doctors were expected to work pro bono, and where most obliged. Segall certainly did. As for his income before the introduction of medicare, Segall is philosophical. “I'd send out bills at the end of the month,” he says. “Those who couldn't pay — it didn't bother me that much.” Segall, who still works 3 days a week in a part of Montreal popular with immigrants and refugees, remembers his own lean years during the Depression when he had to “step out” of medical school at McGill and get a factory job to make ends meet. “I worked as a shipper. Eventually, the boss, the other shipper and the cutter all became my patients.” He also pounded the pavement selling dresses and vacuum cleaners, although he was “not particularly gifted at sales.” Cardiac catheterization was more his line. In 1948 he received a scholarship at the University of Chicago and became one of the first cardiologists to use the technique. At the time, some Canadian research linked heart disease to a diet high in animal proteins and eggs. “Everybody laughed,” says Segall. He would later coauthor a paper on fluctuating blood lipid levels in patients with hypercholesterolemia and coronary artery disease (CMAJ 1960;83:521-4). Recently, Segall made a long-distance phone call a bit too early in the morning. “I apologize from the bottom of my left ventricle,” he deadpanned. Whether it's his early ideas about preventing heart disease with a daily sherry and a joke, or about the dangers of high cholesterol levels, a half-century of practice has clearly given him not only the long view but the last laugh. — Susan Pinker, Montreal

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)0.006
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: none
Teacher disagreement score0.055
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0060.009
Open science0.0010.006
Research integrity0.0080.018
Insufficient payload (model declined to judge)0.0550.025

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 head0.033
GPT teacher head0.232
Teacher spread0.199 · 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".

Quick stats

Citations0
Published2002
Admission routes2
Has abstractyes

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