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Record W2074177020 · doi:10.1177/0272989x0102100101

Randomized Trial of a Patient Decision Aid for Choice of Surgical Treatment for Breast Cancer

2001· article· en· W2074177020 on OpenAlexaff
Vivek Goel, Carol Sawka, Elaine C. Thiel, Elaine H. Gort, Annette M. O’Connor

Bibliographic record

VenueMedical Decision Making · 2001
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of TorontoUniversity of OttawaInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsDecision aidsRandomized controlled trialRegretMedicineBreast cancerMastectomyPsychological interventionPhysical therapySurgeryAlternative medicineCancerNursingInternal medicine

Abstract

fetched live from OpenAlex

A decision aid for the surgical treatment of early breast cancer was evaluated in a randomized controlled trial. The decision aid, a tape and workbook, includes explicit presentation of probabilities, photographs and graphics, and a values clarification exercise. Community surgeons were randomized to use the decision aid or a control pamphlet. Patients completed a questionnaire prior to using the decision aid, after reviewing it but prior to surgery, and 6 months after enrollment. There was no difference in anxiety, knowledge, or decisional regret across the 2 groups. There was a nonsignificant trend toward lower decisional conflict in the decision aid group. A subgroup of women who were initially leaning toward mastectomy or were unsure had lower decisional conflict. Although the decision aid had minimal impact on the main study outcomes, a subgroup may have benefited. Such subgroups should be identified, and appropriate decision support interventions should be developed and evaluated.

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0120.001

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 head0.217
GPT teacher head0.517
Teacher spread0.299 · 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 designRandomized trial
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

Citations183
Published2001
Admission routes1
Has abstractyes

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