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Decision aids for people facing health treatment or screening decisions

2009· review· en· W4243595180 on OpenAlexaff
Annette M. O’Connor, Carol Bennett, Dawn Stacey, Michael J. Barry, Nananda F. Col, Karen Eden, Vikki Entwistle, Valerie Fiset, Margaret Holmes‐Rovner, Sara D. Khangura, Hilary A. Llewellyn‐Thomas, David R. Rovner

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2009
Typereview
Languageen
Field
Topic
Canadian institutionsAlgonquin CollegeUniversity of Ottawa
Fundersnot available
KeywordsDecision aidsMedicineCINAHLPsycINFOMEDLINEPsychological interventionCochrane LibraryFamily medicineRandomized controlled trialMeta-analysisRelative riskAlternative medicineConfidence intervalNursingSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Decision aids prepare people to participate in 'close call' decisions that involve weighing benefits, harms, and scientific uncertainty. OBJECTIVES: To conduct a systematic review of randomised controlled trials (RCTs) evaluating the efficacy of decision aids for people facing difficult treatment or screening decisions. SEARCH STRATEGY: We searched MEDLINE (Ovid) (1966 to July 2006); Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library; 2006, Issue 2); CINAHL (Ovid) (1982 to July 2006); EMBASE (Ovid) (1980 to July 2006); and PsycINFO (Ovid) (1806 to July 2006). We contacted researchers active in the field up to December 2006. There were no language restrictions. SELECTION CRITERIA: We included published RCTs of interventions designed to aid patients' decision making by providing information about treatment or screening options and their associated outcomes, compared to no intervention, usual care, and alternate interventions. We excluded studies in which participants were not making an active treatment or screening decision, or if the study's intervention was not available to determine that it met the minimum criteria to qualify as a patient decision aid. DATA COLLECTION AND ANALYSIS: Two review authors independently screened abstracts for inclusion, and extracted data from included studies using standardized forms. The primary outcomes focused on the effectiveness criteria of the International Patient Decision Aid Standards (IPDAS) Collaboration: attributes of the decision and attributes of the decision process. We considered other behavioural, health, and health system effects as secondary outcomes. We pooled results of RCTs using mean differences (MD) and relative risks (RR) using a random effects model. MAIN RESULTS: This update added 25 new RCTs, bringing the total to 55. Thirty-eight (69%) used at least one measure that mapped onto an IPDAS effectiveness criterion: decision attributes: knowledge scores (27 trials); accurate risk perceptions (11 trials); and value congruence with chosen option (4 trials); and decision process attributes: feeling informed (15 trials) and feeling clear about values (13 trials).This review confirmed the following findings from the previous (2003) review. Decision aids performed better than usual care interventions in terms of: a) greater knowledge (MD 15.2 out of 100; 95% CI 11.7 to 18.7); b) lower decisional conflict related to feeling uninformed (MD -8.3 of 100; 95% CI -11.9 to -4.8); c) lower decisional conflict related to feeling unclear about personal values (MD -6.4; 95% CI -10.0 to -2.7); d) reduced the proportion of people who were passive in decision making (RR 0.6; 95% CI 0.5 to 0.8); and e) reduced proportion of people who remained undecided post-intervention (RR 0.5; 95% CI 0.3 to 0.8). When simpler decision aids were compared to more detailed decision aids, the relative improvement was significant in knowledge (MD 4.6 out of 100; 95% CI 3.0 to 6.2) and there was some evidence of greater agreement between values and choice.In this review, we were able to explore the use of probabilities in decision aids. Exposure to a decision aid with probabilities resulted in a higher proportion of people with accurate risk perceptions (RR 1.6; 95% CI 1.4 to 1.9). The effect was stronger when probabilities were measured quantitatively (RR 1.8; 95% CI 1.4 to 2.3) versus qualitatively (RR 1.3; 95% CI 1.1 to 1.5).As in the previous review, exposure to decision aids continued to demonstrate reduced rates of: elective invasive surgery in favour of conservative options, decision aid versus usual care (RR 0.8; 95% CI 0.6 to 0.9); and use of menopausal hormones, detailed versus simple aid (RR 0.7; 95% CI 0.6 to 1.0). There is now evidence that exposure to decision aids results in reduced PSA screening, decision aid versus usual care (RR 0.8; 95% CI 0.7 to 1.0) . For other decisions, the effect on decisions remains variable.As in the previous review, decision aids are no better than comparisons in affecting satisfaction with decision making, anxiety, and health outcomes. The effects of decision aids on other outcomes (patient-practitioner communication, consultation length, continuance, resource use) were inconclusive.There were no trials evaluating the IPDAS decision process criteria relating to helping patients to recognize a decision needs to be made, understand that values affect the decision, or discuss values with the practitioner. AUTHORS' CONCLUSIONS: Patient decision aids increase people's involvement and are more likely to lead to informed values-based decisions; however, the size of the effect varies across studies. Decision aids have a variable effect on decisions. They reduce the use of discretionary surgery without apparent adverse effects on health outcomes or satisfaction. The degree of detail patient decision aids require for positive effects on decision quality should be explored. The effects on continuance with chosen option, patient-practitioner communication, consultation length, and cost-effectiveness need further evaluation.

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)0.035
metaresearch head score (Gemma)0.155
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.035
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.155
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0060.006
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0030.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0170.001

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 head0.303
GPT teacher head0.474
Teacher spread0.171 · 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 designSystematic review
Domainnot available
GenreReview

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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Citations693
Published2009
Admission routes1
Has abstractyes

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