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Record W2887815063 · doi:10.1111/ijlh.12908

Results of a cross Canada survey of blood film review practice patterns by technologists and pathologists

2018· article· en· W2887815063 on OpenAlexaffabout
David Barth, David C. Good

Bibliographic record

VenueInternational Journal of Laboratory Hematology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Identification and Susceptibility Testing
Canadian institutionsKingston General HospitalToronto General Hospital
Fundersnot available
KeywordsMedicineBlood filmFamily medicinePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: No common or widely accepted criteria exist for physician review of blood films or for reported standards of physician rate of blood film review. Individual institutions generally have internal criteria for physician review of blood films. To better understand how and why blood film reviews are performed at different institutions across Canada, with a specific interest in physician blood film review, we undertook a survey to assess the current practise patterns of physician review of blood films across Canada. METHODS: A 15 question survey was developed and sent to 24 academic, large community and corporate laboratories across Canada by e-mail to laboratory directors of those institutions. Centres were chosen to include all provinces in Canada and most of the major academic centres. Twenty of the 24 centres responded. RESULTS: The mean rate of physician review of blood films as a percentage of all CBCs processed per day was 4.1% (range 0.35%-13%) and of all blood films made per day was 28.8% (range 1.9%-66.5%). Data on factors which might affect physician review rates, physician preferences regarding what percentage of physician blood film review rates is reasonable and physician perspectives on the role of physician blood film review are discussed. CONCLUSION: This survey reveals that there is varying practise patterns and opinions with respect to blood film review by physicians in Canada.

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)0.001
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.327
Teacher spread0.312 · 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 designBench or experimental
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
Published2018
Admission routes2
Has abstractyes

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