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Record W2539537051 · doi:10.1186/s13643-016-0364-8

Methods for estimating the burden of antimicrobial resistance: a systematic literature review protocol

2016· article· en· W2539537051 on OpenAlexaboutno aff
Nichola R. Naylor, Sachin Silva, Kavian Kulasabanathan, Rifat Atun, Nina Zhu, Gwenan M. Knight, Julie V. Robotham

Bibliographic record

VenueSystematic Reviews · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsnot available
FundersPublic Health EnglandImperial College LondonNational Institute for Health and Care ResearchNational Institute for Health Research Health Protection Research Unit
KeywordsMedicineProtocol (science)Antibiotic resistanceSystematic reviewAntimicrobialMEDLINEIntensive care medicineAlternative medicineAntibioticsPathologyMicrobiology

Abstract

fetched live from OpenAlex

BACKGROUND: Estimates of the burden of antimicrobial resistance (AMR) are needed to ascertain AMR impact, to evaluate interventions, and to allocate resources efficiently. Recent studies have estimated health, cost, and economic burden relating to AMR, with outcomes of interest ranging from drug-bug resistance impact on mortality in a hospital setting to total economic impact of AMR on the global economy. However, recent collation of this information has been largely informal, with no formal quality assessment of the current evidence base (e.g. with predefined checklists). This review therefore aims to establish what perspectives and resulting methodologies have been used in establishing the burden of AMR, whilst also ascertaining the quality of these studies. METHODS: The literature review will identify relevant literature using a systematic review methodology. MEDLINE, EMBASE, Scopus and EconLit will be searched utilising a predefined search string. Grey literature will be identified by searching within a predefined list of organisational websites. Independent screening of retrievals will be performed in a two-stage process (abstracts and full texts), utilising a pre-defined inclusion and exclusion criteria. Data will be extracted into a data extraction table and descriptive examination will be performed. Study quality will be assessed using the Newcastle-Ottawa scales and the Philips checklists where appropriate. A narrative synthesis of the results will be presented. DISCUSSION: This review will provide an overview of previous health, cost and economic definitions of burden and the resultant impact of these different definitions on the burden of AMR estimated. The review will also explore the methods that have been used to calculate this burden and discuss resulting study quality. This review can therefore act as a guide to methods for future research in this area. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016037510.

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)0.147
metaresearch head score (Gemma)0.141
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: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.853
Threshold uncertainty score0.775

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1470.141
Meta-epidemiology (narrow)0.0060.006
Meta-epidemiology (broad)0.0150.017
Bibliometrics0.0260.021
Science and technology studies0.0050.006
Scholarly communication0.0090.011
Open science0.0070.009
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0930.020

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 head0.029
GPT teacher head0.373
Teacher spread0.344 · 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
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

Citations16
Published2016
Admission routes1
Has abstractyes

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