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Record W1504695419 · doi:10.5772/38569

The Risk of Blood-Borne Viral Infection due to Syringe Re-Use

2012· book-chapter· en· W1504695419 on OpenAlexaffabout
Tamer Oraby, Susie ElSaadany, Robert Gervais, Mustafa Al‐Zoughool, G. Michael, Lynn Johnston, Mel Krajden, Dick Zoutman, Jun Wu, Daniel Krewski

Bibliographic record

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsQueen's UniversityUniversity of British ColumbiaBC Centre for Disease ControlQueen Elizabeth II Health Sciences CentrePublic Health Agency of CanadaUniversity of Ottawa
Fundersnot available
KeywordsSyringeGovernment (linguistics)MedicineTransmission (telecommunications)Medical emergencyEmergency medicineFamily medicinePsychiatryEngineering

Abstract

fetched live from OpenAlex

In the fall of 2008, light was shed on the practice of syringe re-use occurring in western Canada (Government of Alberta, 2009). In this situation, syringes had been re-used between patients to administer sedating medication through patient intravenous (IV) lines (Government of Alberta, 2009). Later it was reported that other incidents of syringe re-use had occurred in Canada (CBC News-Edmonton, 2008a;CBC News-Edmonton, 2008b). The question arose of whether this practice may have resulted in the transmission of bloodborne pathogens to patients and, if so, how many and with what level of risk. To answer this question, a retrospective study involving approximately 1,400 patients was undertaken (Government of Alberta, 2009). However, questions were also raised as to whether estimates based on modeling scenarios could provide information to guide decisions on the need for look-backs.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.059
GPT teacher head0.377
Teacher spread0.318 · 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 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

Citations3
Published2012
Admission routes2
Has abstractyes

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