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Record W2163687711 · doi:10.1186/2046-4053-2-104

A surveillance system to assess the need for updating systematic reviews

2013· article· en· W2163687711 on OpenAlexafffund
Nadera Ahmadzai, Sydne J Newberry, Margaret Maglione, Alexander Tsertsvadze, Mohammed T Ansari, Susanne Hempel, Aneesa Motala, Sophia Tsouros, Jennifer J. Schneider Chafen, Roberta Shanman, David Moher, Paul G Shekelle

Bibliographic record

VenueSystematic Reviews · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsOttawa Hospital
FundersHealth CanadaUniversity of OttawaAgency for Healthcare Research and QualityU.S. Department of Health and Human Services
KeywordsMedicineSystematic reviewAgency (philosophy)Medical emergencyExpert opinionMedical physicsMEDLINEIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Systematic reviews (SRs) can become outdated as new evidence emerges over time. Organizations that produce SRs need a surveillance method to determine when reviews are likely to require updating. This report describes the development and initial results of a surveillance system to assess SRs produced by the Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) Program. METHODS: Twenty-four SRs were assessed using existing methods that incorporate limited literature searches, expert opinion, and quantitative methods for the presence of signals triggering the need for updating. The system was designed to begin surveillance six months after the release of the original review, and then ceforth every six months for any review not classified as being a high priority for updating. The outcome of each round of surveillance was a classification of the SR as being low, medium or high priority for updating. RESULTS: Twenty-four SRs underwent surveillance at least once, and ten underwent surveillance a second time during the 18 months of the program. Two SRs were classified as high, five as medium, and 17 as low priority for updating. The time lapse between the searches conducted for the original reports and the updated searches (search time lapse - STL) ranged from 11 months to 62 months: The STL for the high priority reports were 29 months and 54 months; those for medium priority reports ranged from 19 to 62 months; and those for low priority reports ranged from 11 to 33 months. Neither the STL nor the number of new relevant articles was perfectly associated with a signal for updating. Challenges of implementing the surveillance system included determining what constituted the actual conclusions of an SR that required assessing; and sometimes poor response rates of experts. CONCLUSION: In this system of regular surveillance of 24 systematic reviews on a variety of clinical interventions produced by a leading organization, about 70% of reviews were determined to have a low priority for updating. Evidence suggests that the time period for surveillance is yearly rather than the six months used in this project.

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)0.490
metaresearch head score (Gemma)0.703
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.510
Threshold uncertainty score0.629

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4900.703
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0620.032
Science and technology studies0.0040.002
Scholarly communication0.0090.012
Open science0.0050.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.002

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 head0.705
GPT teacher head0.514
Teacher spread0.192 · 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 designSimulation or modeling
DomainEvaluation
GenreMethods

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

Citations34
Published2013
Admission routes2
Has abstractyes

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