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Record W7117440269

Possible algorithms for determining adverse reactions caused by food supplements in Romania

2021· article· ru· W7117440269 on OpenAlexaboutno aff
Lilia NAGY

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2021
Typearticle
Languageru
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsnot available
Fundersnot available
KeywordsEpidemiologyAntibioticsSurgical proceduresAntibiotic prophylaxisAdverse effectHealth careSurgical woundMEDLINEIncidence (geometry)
DOInot available

Abstract

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Introduction. Surgical site infection (SSI) is a major patient safety concern in hospitals, with implications on patient morbidity, mortality and increased health costs. The analyzed studies demonstrated that the patients with SSI are twice as likely to die, 60% more likely to be admitted to the intensive care unit, and more than five times more likely to be readmitted to the hospital after discharge. The pathogens causing SSI may be caused by endogenous or exogenous origin and are typically similar to other healthcare-associated infections. Consequently, most SSIs are caused by S. aureus, the coagulase negative staphylococci, enterococci and E. coli Epidemiological findings demonstrated that the most SSIs can be attributed to a variety of factors which can be classified into patient-related, procedure-related and others. Material and methods. This paper analyzes the major aspects of this topic published last 10 years and were based on 98 bibliographic sources of authors across the country and abroad using Academic Google, PubMed databases (USA, France, Italy, Germany Canada, Romania, etc.). Results. The authors of recent studies have defined the surgical site infections as infections occurring up to 30 days after surgery (or up to one year after surgery in patients receiving implants) and affecting either the incision or deep tissue at the operation site. Despite modern surgical techniques, use of prophylactic antibiotics pre- and postoperatively and other preventive measures, SSI remains a burden for the patient and health system. Most SSI may be caused by endogenous organisms within the patient’s body that are exposed during surgery that depend on surgical site (ex. the risk of developing SSI from enteric Gram- bacteria increases with surgery on the gastrointestinal tract). SSI caused by exogenous bacteria are related with contaminated surgical instruments, operating room surfaces, air, personnel. It was found that periodic surveillance and feedback for surgeons on SSIs rate and associated factors can decrease up to 50% of cases. Most studies demonstrated that the SSIs can be attributed to risk factors inherent to the patient and procedure and other (ex. volume of surgeries performed in the department, the season, indications for surgery, the working environment in the operation room. Antimicrobial stewardship programs (ASPs) are essential to reduce SSI rates and antimicrobial resistance. The microbiology department needs to establish the local resistance patterns and to identify the most common organisms likely to be encountered. It should be every practitioner’s priority to use antibiotics judiciously and to de-escalate from empiric antibiotics as soon as the sensitivity results are available. Surveillance and control of SSI assume standardized definitions, rapid method of diagnoses, effective surveillance programs and stratification of the SSI rates according to risk factors associated with the development of SSI. Conclusions. SSIs remain a burden to postoperative patients and that implementation of interventions aimed at promoting appropriate and evidence-based use of antimicrobials are needed. Effective management of SSI requires a team approach including the patient, ward staff, reception staff, nursing staff, surgeons, ICU clinicians, cleaners, maintenance team etc.

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)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0090.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.260
GPT teacher head0.559
Teacher spread0.299 · 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 designSimulation or modeling
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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