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Decision-Making Process--Communicating Risk/Benefits: Is There an Ideal Technique?

2001· review· en· W2111116561 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJNCI Monographs · 2001
Typereview
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster UniversityCancer Care Ontario
Fundersnot available
KeywordsMedicineIdeal (ethics)Process (computing)MEDLINERisk assessmentDecision-makingRisk analysis (engineering)Intensive care medicineOperations managementEpistemologyComputer securityComputer science

Abstract

fetched live from OpenAlex

During the last decade, there have been major advances in the treatment of early-stage breast cancer. The decisions a patient now must make concerning her treatment are often difficult and complex, e.g., mastectomy versus lumpectomy plus breast ra-diation therapy, adjuvant chemotherapy and/or hormonal therapy versus no further treatment, regional radiation therapy or no regional radiation therapy. In the past, physicians tended to make decisions for patients with little patient input. More recently, women have indicated the need for more information about their disease and a desire to be involved in decisions about their care (1). Degnar et al. (2) examined the preferences of 1012 women with breast cancer for participation in treatment decision making. Twenty-two percent of the women wanted to select their own cancer treatment (active role), 44 % wanted to select their treatment collaboratively with

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.004
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0050.002
Research integrity0.0020.007
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 head0.273
GPT teacher head0.509
Teacher spread0.236 · 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