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Record W1991291241 · doi:10.1111/add.12131

The importance of indirect screening and objective gold standards: a response to <scp>T</scp>erplan (2012)

2013· letter· en· W1991291241 on OpenAlexaff
Steven J. Ondersma, Dace S. Svikis, James M. LeBreton, David L. Streiner, Emily R. Grekin, Phebe Lam, Veronica Connors‐Burge

Bibliographic record

VenueAddiction · 2013
Typeletter
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGold standard (test)PsychologyMedicineSample (material)Clinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

We wish to thank Dr Terplan for his comments 1 on our recent paper 2, and offer here a brief response. Our paper suggests that self-report during a structured interview is a poor gold standard for screener validation when under-reporting is likely. That is, the validity of direct screeners in such contexts may be falsely inflated by comparisons to an overly similar gold standard, resulting in high accuracy values despite the possibility that many may deny drug use during the interview as well as during screening. Using an objective gold standard with a sample of African American women, we demonstrated that a commonly used direct screener failed to identify the majority of those using drugs during pregnancy, and that a rigorously developed indirect screener identified a far greater proportion of at-risk women. Given our clear distinction between self-report and objective gold standards and the very poor performance of direct screening in our sample, it is surprising that Dr Terplan questions the need for a measure such as the Wayne Indirect Drug Use Screener (WIDUS). The direct screener in our study identified only 7% of women using drugs during pregnancy (37% when using its lowest possible cut score). It is difficult to see how this can be considered anything other than unacceptable. Further, the existence of other screeners with similar predictive accuracy is of limited relevance to the WIDUS if those accuracy values were derived against self-report gold standards. Dr Terplan fails to recognize this crucial distinction. Whether or not we need more screeners validated against self-report, we very much need more research on safe, practical ways to identify actual drug use during pregnancy. Dr Terplan's additional concerns relate to what might follow a positive indirect screen. Anticipating such questions, we wrote originally that: ‘Brief interventions that address substance use in a non-specific way, in the context of several other potential health risks, may be helpful … Future research should evaluate whether it is possible for a brief intervention to reduce drug use without directly presuming the presence of that behavior’ ([2], p. 2105). We are in the midst of a clinical trial testing just such an indirect brief intervention. Although outcome data are not yet available, 46 of 52 intervention group participants thus far (85%) report being more likely to make a personal change because of the intervention, with most specifically indicating a desire to reduce drug use in their home; this despite the lack of any presumption of drug use. Further, indirect screening actually presents far less risk to pregnant women than traditional approaches. Endorsing items such as: ‘Most of my friends smoke cigarettes’ is, by design, considerably less prejudicial than a positive urinalysis or direct admission of drug use. It would be irresponsible to simply ignore the many at-risk pregnant women who choose not to disclose drug use, or to respond only with bromides about education and empowerment. Creative approaches can and should be pursued vigorously. None.

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)0.044
metaresearch head score (Gemma)0.154
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.956
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.154
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0080.010
Scholarly communication0.0080.008
Open science0.0060.005
Research integrity0.0630.088
Insufficient payload (model declined to judge)0.0050.003

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 head0.009
GPT teacher head0.248
Teacher spread0.238 · 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.

Study designNot applicable
DomainMethods
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

Citations2
Published2013
Admission routes1
Has abstractyes

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