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Record W2743269491 · doi:10.1186/s12916-017-0903-8

Consistency and sources of divergence in recommendations on screening with questionnaires for presently experienced health problems or symptoms: a comparison of recommendations from the Canadian Task Force on Preventive Health Care, UK National Screening Committee, and US Preventive Services Task Force

2017· article· en· W2743269491 on OpenAlexaffabout
Brett D. Thombs, Nazanin Saadat, Kira E. Riehm, Justin M. Karter, Akansha Vaswani, Bonnie K. Andrews, Peter Simons, Lisa Cosgrove

Bibliographic record

VenueBMC Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicHealth Promotion and Cardiovascular Prevention
Canadian institutionsJewish General HospitalMcGill University
Fundersnot available
KeywordsMedicineTask forceConsistency (knowledge bases)Family medicineHealth careMEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: Recently, health screening recommendations have gone beyond screening for early-stage, asymptomatic disease to include "screening" for presently experienced health problems and symptoms using self-report questionnaires. We examined recommendations from three major national guideline organizations to determine the consistency of recommendations, identify sources of divergent recommendations, and determine if guideline organizations have identified any direct randomized controlled trial (RCT) evidence for the effectiveness of questionnaire-based screening. METHODS: We reviewed recommendation statements listed by the Canadian Task Force on Preventive Health Care (CTFPHC), the United Kingdom National Screening Committee (UKNSC), and the United States Preventive Services Task Force (USPSTF) as of 5 September 2016. Eligible recommendations focused on using self-report questionnaires to identify patients with presently experienced health problems or symptoms. Within each recommendation and accompanying evidence review we identified screening RCTs. RESULTS: We identified 22 separate recommendations on questionnaire-based screening, including three CTFPHC recommendations against screening, eight UKNSC recommendations against screening, four USPSTF recommendations in favor of screening (alcohol misuse, adolescent depression, adult depression, intimate partner violence), and seven USPSTF recommendations that did not recommend for or against screening. In the four cases where the USPSTF recommended screening, either the CTFPHC, the UKNSC, or both recommended against. When recommendations diverged, the USPSTF expressed confidence in benefits based on indirect evidence, evaluated potential harms as minimal, and did not consider cost or resource use. CTFPHC and UKNSC recommendations against screening, on the other hand, focused on the lack of direct evidence of benefit and raised concerns about harms to patients and resource use. Of six RCTs that directly evaluated screening interventions, five did not report any statistically significant primary or secondary health outcomes in favor of screening, and one trial reported equivocal results. CONCLUSIONS: Only the USPSTF has made any recommendations for screening with questionnaires for presently experienced problems or symptoms. The CTFPHC and UKNSC recommended against screening in all of their recommendations. Differences in recommendations appear to reflect differences in willingness to assume benefit from indirect evidence and different approaches to assessing possible harms and resource consumption. There were no examples in any recommendations of RCTs with direct evidence of improved health outcomes.

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)0.388
metaresearch head score (Gemma)0.715
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.755

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3880.715
Meta-epidemiology (narrow)0.0010.003
Meta-epidemiology (broad)0.0060.010
Bibliometrics0.0230.018
Science and technology studies0.0030.004
Scholarly communication0.0070.005
Open science0.0100.007
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.377
Teacher spread0.323 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
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

Citations52
Published2017
Admission routes2
Has abstractyes

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