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Record W1768317251

Assessing gender bias in acute medical care in Canada

2010· dissertation· en· W1768317251 on OpenAlexaboutno aff
Emily L. Eaton

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2010
Typedissertation
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRevascularizationMyocardial infarctionKidney diseaseDialysisMedical recordDemographicsPercutaneous coronary interventionRetrospective cohort studyEmergency medicineAcute coronary syndromeCoronary artery diseaseInternal medicineDemography
DOInot available

Abstract

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Background: Gender bias has been explored extensively in the treatment of cardiovascular disease (CVD), particularly acute myocardial infarction (AMI). Previous research is inconsistent in suggesting that women who suffer an AMI are treated less optimally than men. To date, gender bias has not been well addressed in other CVDs, specifically cerebrovascular accident (CVA), coronary revascularization, or chronic kidney disease (CKD). In an attempt to get a clearer grasp of the problem, community acquired pneumonia (CAP) was also studied and served as a non-vascular disease control. -- Methods: Using trained research nurses, retrospective chart reviews were completed for all patients admitted with AMI, CVA, for coronary revascularization, or CAP in 1995/6, 1998/9, and 2000/1 in two locations of Newfoundland (St. John's and Central Newfoundland). CKD care was analyzed using data from the STARRT (Study To Assess Renal Replacement Therapy) study, a Canadian multicentre retrospective chart review of incident dialysis patients followed for six months. All results were divided into three categories - access, intervention, and outcome - and, after controlling for baseline demographics, were explored for differences in care between men and women. -- Results: Women were often older than men and suffered from more co-morbidities and more severe medical histories, except for CAP where men were more likely to have co-morbid illnesses. Women who had suffered an AMI had a significantly longer time to thrombolytics (70 vs. 45 mins., p=0.02) and were less likely to be admitted to the CCU (84% vs. 91%, p=0.02). Women with severe CAD were more likely to receive medical management (42% vs. 28%, p=0.005) and, of the women who did receive CABG, had longer wait times in one priority for CABG group (8.0 vs. 6.0 days, p=0.05). Women began dialysis with a lower eGFR level (8.6 vs. 10.1 mL/mins., p=0.006) after receiving less pre-dialysis care than men (>1 mth. vs. <1 mth.) (80% vs. 88%, p=0.05). There was no evidence of a gender bias against women in CVA patients, in fact women were more likely to be seen by a social worker than men (43% vs. 34%, p=0.01). Of the patients receiving treatment for CAP, women were less likely than men to receive the appropriate antibiotics (AB) when considering the 1993 guidelines for AB treatment (70% vs. 78%, p=0.03). While women received less optimal access and interventions, the proportion of death between men and women for all CVDs analyzed were similar. -- Conclusions: Women are not treated with the same quality of care as men with regards to access and intervention for AMI's, coronary revascularization, and CKD. This bias was also found in a nonvascular control group. A potential gender bias in the treatment of these patients needs to be explored further.

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)0.009
metaresearch head score (Gemma)0.031
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 score0.058
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.084
GPT teacher head0.355
Teacher spread0.271 · 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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