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Record W7024920690

Surveillance of central line-associated bloodstream infections in Quebec intensive care units

2012· dissertation· en· W7024920690 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2012
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicRare-earth and actinide compounds
Canadian institutionsnot available
Fundersnot available
KeywordsIncidence (geometry)Bloodstream infectionIntensive careEpidemiologyEpidemiological surveillanceInfection control
DOInot available

Abstract

fetched live from OpenAlex

Central line-associated bloodstream infections (CLABSI) figure as one of the most important healthcare-associated infections (HAI), particularly in intensive care units (ICU). Despite their clinical and public health importance, little is known about CLABSI in Canadian ICUs. Thus, the first objective of this thesis was to describe the epidemiology of CLABSI in Quebec ICUs, using data from the Surveillance Provinciale des Infections Nosocomiales – Bactériémies Associées aux Cathéters Centraux (SPIN-BACC) program. We showed that CLABSIs are an important problem in Quebec ICUs, but CLABSI incidence rates have decreased since 2007. Moreover, the proportion of methicillin-resistant Staphylococcus aureus has declined to <40% since 2006 (chapter 6). Surveillance programs are essential to establish benchmarks. In the last years, several regional and national CLABSI surveillance programs have decided to eliminate continuous participation requirements from hospitals. This might have jeopardized the validity of these programs' results because the minimal number of months hospitals should participate in such programs to generate valid annual benchmarks for CLABSI incidence rates have yet to be determined. Our second objective was to determine, through simulation, the impact of different participation requirements on the ability of national and provincial/regional surveillance programs to yield valid estimates of the true annual ICU CLABSI pooled incidence rates. We demonstrated that shortening participation requirements might be suitable for national ICU CLABSI surveillance programs if data are randomly collected. Nevertheless, regional/provincial programs should opt for continuous participation to avoid biased benchmarks (chapter 7). Furthermore, surveillance programs can also be used as a tool to reduce CLABSI incidence rates in ICUs. However, the magnitude of this effect has not been definitely determined as earlier studies presented a wide range of effect estimates. We hypothesized that the effect of surveillance on CLABSI rates differs depending on the characteristics of participating ICUs. Our third objective was to determine the effect of SPIN-BACC on the CLABSI incidence rates in Quebec ICUs, and identify ICU-level variables associated with higher CLABSI incidence rates. There were important reductions in the CLABSI incidence rates of "surveillance-naïve" (31%) and of non-university affiliated ICUs (27%) that participated in SPIN-BACC for 3 years. However, due to our small sample size, these results were not statistically significant. Neonatal and "surveillance-naïve" units were associated with higher CLABSI incidence rates (chapter 8). In conclusion, our first study described the CLABSI burden on ICU patients in Quebec. Our simulation study suggested that small and medium sizes surveillance programs should perform continuous surveillance to avoid biased benchmarks. Finally, we suggested that reductions in ICU CLABSI incidence rates associated with targeted surveillance may be more pronounced among "surveillance-naïve" and non-university affiliated ICUs. All the different applications of CLABSI surveillance data demonstrated in this thesis have the ultimate goal of improving patient care and safety.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.239
Teacher spread0.227 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
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

Citations0
Published2012
Admission routes1
Has abstractyes

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