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Abstract P6-07-01: Are patient perceptions and expectations about peri-operative imaging for metastatic breast cancer in keeping with current guidelines?

2013· article· en· W1965468254 on OpenAlexaffabout
Demetrios Simos, B Hutton, Sasha Mazzarello, Ian D. Graham, J-M Caudrelier, SZ Gertler, P Wheatley-Price, Roanne Segal-Nadler, Shiv P. Verma, Xinwei Song, Iryna Kuchuk, M. Clemons

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicinePerioperativeConcordanceStage (stratigraphy)AbdomenBreast cancerAsymptomaticRadiologyCancerDiseaseSurgeryInternal medicine

Abstract

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Abstract Background: The probability of detecting radiologically evident distant metastatic disease in asymptomatic women with primary operable breast cancer is low. Because of this, evidence-based guidelines have been developed to guide physicians on whom to image. Despite these guidelines, peri-operative staging imaging is frequently over-utilized. Relatively little is known about what patients’ perceptions and expectations are regarding peri-operative imaging and whether or not their views are in concordance with the guidelines. We undertook this study in an attempt to answer this question. Methods: A questionnaire on peri-operative imaging to look for distant metastatic disease was given to women with early stage breast cancer who had completed their surgery. The survey questions were developed in a collaborative effort amongst oncologists, epidemiologists and knowledge translation experts. Results: Over a 3 month period, 234 surveys were completed at a large Canadian academic cancer centre. The use of peri-operative imaging to assess the skeleton (e.g. bone scan), thorax (e.g. CT, xray), and abdomen (e.g. CT, MRI or ultrasound) is summarized in Table 1 for the 187 patients (80%) who identified their disease stage. Patient reported perioperative imaging by disease stage Stage 1Stage 2Stage 3No. of patients (%)82/187 (44%)67/187 (36%)38/187 (20%)Median age (range)59 (29-80)57 (27-77)56 (49-65)Peri-operative imaging done for Skeleton in (%)41/82 (50%)47/67 (70%)33/38 (87%)Thorax in (%)48/82 (59%)53/67 (79%)30/38 (79%)Abdomen in (%)34/82 (41%)42/67 (63%)27/38 (71%) The relative proportion of patients undergoing imaging increased with advancing stage. Of the 187 patients, 66% indicated they would want imaging if the chance of finding metastatic disease was ≤10% and half of these patients (i.e. 33%) indicated they would want imaging if the chance was <1%. The most common factors identified as being either extremely (EI) or very important (VI) by patients were: catching the spread of cancer to other parts of the body early (93%), reducing the chance of dying (90%), and providing peace of mind (77%). Avoiding inconvenience, exposure to scans, and extra imaging that will not change length or quality of life, and false alarms were EI or VI in ∼50%. Perceptions of these factors did not differ across disease stage. Although 85% indicated doing whatever their doctor recommended was either EI or VI to them, 72% indicated that they would feel very or somewhat uncomfortable if their physician did not order imaging to look for metastatic disease, even if this was in keeping with the guideline recommendation. Conclusion: Irrespective of evidence-based guidelines, many patients undergo peri-operative imaging. While guidelines tend to address physician behaviour it is evident that patient perceptions and expectations are divergent from the evidence-based guidelines. If patient expectations are, in part, driving excessive imaging, new strategies targeting patient expectations and knowledge are required. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P6-07-01.

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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)0.004
metaresearch head score (Gemma)0.020
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 score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.064
GPT teacher head0.431
Teacher spread0.368 · 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".

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Citations0
Published2013
Admission routes2
Has abstractyes

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