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Record W2917178290

Routine preoperative electrocardiogram and chest x-ray prior to elective surgery in Alberta, Canada Electrocardiogramme et radiographie des poumons preoperatoires de routine avant une chirurgie non urgente en Alberta, Canada

2010· article· fr· W2917178290 on OpenAlexaboutno aff
Nguyễn Xuân Thành, Egon Jonsson

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineElective surgeryGeneral surgeryEmergency medicineSurgery
DOInot available

Abstract

fetched live from OpenAlex

Purpose A study was undertaken to evaluate the utilization rates of routine preoperative electrocardiogram (ECG) and chest x-ray (CXR) by sex, age, and most frequent surgery type, and to estimate the total cost of these screening tests. Methods We included all patients undergoing elective surgery in Alberta from April 1, 2005 to March 31, 2007, except those with a cancer, trauma, or cardiac diagnosis. The utilization rate was equal to the number of tests divided by the number of elective surgeries. The total cost of the tests was estimated in Canadian dollars under a health care perspective and was equal to the number of tests multiplied by the cost per test. Results With utilization rates of 13.4% and 23.2%, routine preoperative ECG and CXR tests cost Alberta about $369,000 and $637,000 over 2 yrs, respectively. More than 80% of the cost was incurred by tests on patients aged 50 or older. The utilization rates of tests vary considerably among the most frequent surgeries, but not between men and women. Conclusions Routine preoperative testing rates and costs are relatively low in Alberta. It is possible that general evidence widely disseminated over the past number of

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2010
Admission routes1
Has abstractyes

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