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Record W2738390891 · doi:10.1093/jnci/djs409

Response

2012· article· en· W2738390891 on OpenAlexaff
Stacey L. Hart, Michael A. Hoyt, Michael A. Diefenbach, Denise Anderson, Kristin Kilbourn, Lynette L. Craft, Jennifer L. Steel, Pim Cuijpers, David C. Mohr, Mark Berendsen, Bonnie Spring, Annette L. Stanton

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2012
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

We appreciate Coyne highlighting several of our original points, including the fundamental importance of the research question and the strikingly small body of relevant randomized controlled trials (RCTs). We agree that each included RCT had limitations. We concur that dissemination into routine care requires additional carefully formulated research. Two primary points of disagreement with Coyne remain. First, we contend that including collaborative care RCTs and trials with relatively small samples was well reasoned. Our goal (p. 991) was to examine the efficacy of RCTs testing various therapeutic approaches rather than specific psychotherapies. Collaborative care (CC) interventions are well suited for primary care ( 1 ) and are gaining traction in oncology ( 2 ). Secondary processes in CC, such as education about depression, are common components of psychotherapy ( 3 ). In the three CC trials, patients were randomly assigned to CC or usual care. We emphasized (p. 1000) that patients do not invariably receive psychotherapy in a CC model but rather can receive psychotherapy, medication, or both. Most CC patients received psychotherapy, with or without medication. Having treatment options better represents the naturalistic context and fosters successful dissemination to practice. Moreover, attending to patients’ preferences for depression treatment can yield positive outcomes ( 4 , 5 ). It is notable, however, that problem-solving therapy (all delivered within CC) had statistically significantly less impact on depressive symptoms than did cognitive behavioral therapy. Allowing RCTs with relatively small sample sizes, which coincides with our inclusion of pharmacologic studies and the well-documented knowledge of substantial attrition in pharmacologic RCTs for major depressive disorder ( 6 ), reflects a decision about which active debate exists in the meta-analytic literature ( 7 ). Our use of Hedges’ g , which corrects for small sample bias, and findings from our elected safeguards of examining publication bias, the fail-safe N , and whether the psychotherapeutic RCT effects varied as a function of trial attrition all suggest a stable overall effect size. A second disagreement regards a statistical decision. As we stated (p. 992), because interventions were distinct, we calculated two separate effect sizes for trials containing two intervention groups, which violates the assumption of independent effect sizes. We conducted sensitivity analyses to address this issue; separate analyses including only the largest or the smallest effect size from those studies did not substantially influence the findings (p. 999). Do the limitations of existing RCTs, as illuminated in our original article, and our analytic decisions render the meta-analytic findings unreliable or invalid? Our adoption of conservative analytic approaches and methodologic and quantitative safeguards leads us to affirm the finding of “reliable positive effects” of psychotherapeutic and pharmacologic interventions for adults with cancer and elevated depressive symptoms. Rather than offering a definitive verdict, this meta-analysis provides a foundation of evidence from which to build. Enhancing intervention efficacy and efficiency is urgent in light of the burden of depression and the exigencies of a changing health-care environment. Ideally, Hart et al. and Coyne will motivate researchers to apply rigorous standards for conducting RCTs and applying evidence-based interventions to address depression in individuals confronting cancer.

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)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.217
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.055
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.004
Open science0.0030.004
Research integrity0.0110.013
Insufficient payload (model declined to judge)0.2170.108

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 head0.144
GPT teacher head0.476
Teacher spread0.332 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2012
Admission routes1
Has abstractyes

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