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Record W4290053050 · doi:10.2196/38386

The Pragmatic Return to Effective Dental Infection Control Through Triage and Testing (PREDICT) Study: Protocol for a Prospective Clinical Study in the National Dental Practice–Based Research Network

2022· article· en· W4290053050 on OpenAlexvenueno aff
Janine Fredericks-Younger, Daniel H. Fine, Gayathri Subramanian, Modupe O. Coker, Cyril Meyerowitz, Patricia Ragusa, Veerasathpurush Allareddy, Mary Ann McBurnie, Ellen Funkhouser, Maria Laura Gennaro, Cecile A. Feldman

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsnot available
FundersNational Institute of Dental and Craniofacial ResearchNational Institutes of Health
KeywordsTriageMedicineProtocol (science)PandemicInfection controlWorkforceHealth careFamily medicinePrivate practiceDental practiceMedical emergencyCoronavirus disease 2019 (COVID-19)DentistryAlternative medicineIntensive care medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Dental practice has been greatly affected by the COVID-19 pandemic. As SARS-CoV-2 infection is transmitted by respiratory fluids, dental practice techniques, which include aerosol-generating procedures, can increase the risk of transmission causing heightened safety concerns for both dental health care workers (DHCWs) and patients. These concerns have resulted in the reduction in patient volume and the available workforce within dental practices across the United States. Standardized methods for COVID-19 triage and testing may lead to increased safety and perceptions of safety for DHCWs and their patients and promote willingness to provide and access oral health care services. OBJECTIVE: This study is designed to develop procedures that test the feasibility of enhanced COVID-19 triage and testing in dental offices. It will provide preliminary data to support a larger network-wide study grant application aimed at developing protocols to address safety concerns of patients and DHCWs in a peri-COVID-19 pandemic era. METHODS: The feasibility study is being conducted in 4 private dental practices, each of which has a dentist member of the National Dental Practice-Based Research Network. Participants include the DHCWs and patients of the dental practice. Study procedures include completion of COVID-19 triage, completion of COVID-19 testing (point-of-care [POC] or laboratory-based [LAB] SARS-CoV-2 viral, antigen, and antibody tests based on office designation), and administration of perception and attitude surveys for participating DCHWs and patients of the dental practice over a defined study period. The office designation and the participant's role in the practice determines which testing protocol is executed within the office. There are 4 study groups following 4 distinct protocols: (1) POC DHCWs, (2) POC patients, (3) LAB DHCWs, and (4) LAB patients. RESULTS: Data collection began in December of 2021 and concluded in March 2022. Study results are expected to be published in fall 2022. CONCLUSIONS: The results of this feasibility study will help identify the viability and functionality of COVID-19 triage and testing in dental practices and inform a larger network-wide study grant application that develops protocols that address safety concerns of patients and DHCWs in a COVID-19 environment. TRIAL REGISTRATION: ClinicalTrials.gov NTC05123742; https://clinicaltrials.gov/ct2/show/NCT05123742?term=NCT05123742. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/38386.

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)0.079
metaresearch head score (Gemma)0.066
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: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.079
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.066
Meta-epidemiology (narrow)0.0070.005
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0040.005
Science and technology studies0.0060.005
Scholarly communication0.0060.006
Open science0.0050.003
Research integrity0.0090.009
Insufficient payload (model declined to judge)0.0710.021

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 head0.411
GPT teacher head0.659
Teacher spread0.248 · 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
GenreProtocol

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
Published2022
Admission routes1
Has abstractyes

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