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Record W1523299349 · doi:10.1002/pbc.25613

The pediatric hematology/oncology educational laboratory in‐training examination (PHOELIX): A formative evaluation of laboratory skills for Canadian pediatric hematology/oncology trainees

2015· article· en· W1523299349 on OpenAlexafffundabout
Elaine Leung, David Dix, Jason C. Ford, D Barnard, Eileen McBride

Bibliographic record

VenuePediatric Blood & Cancer · 2015
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsBC Children's HospitalUniversity of British ColumbiaChildren's Hospital of Eastern OntarioMontfort HospitalUniversity of Ottawa
FundersC17 Children's Cancer and Blood Disorders
KeywordsMedicineHematologyFormative assessmentPediatric oncologyInternal medicineClinical OncologyOncologyMedical physicsCancerPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Pediatric hematologists/oncologists need to be skilled clinicians, and must also be adept and knowledgeable in relevant areas of laboratory medicine. Canadian training programs in this subspecialty have a minimum requirement for 6 months of training in acquiring "relevant laboratory diagnostic skills." The Canadian pediatric hematology/oncology (PHO) national specialty society, C17, recognized the need for an assessment method in laboratory skills for fellows graduating from PHO training programs. PROCEDURE: Canadian pediatric hematologists/oncologists were surveyed regarding what were felt to be the essential laboratory-related knowledge and skills deemed necessary for graduating pediatric hematology/oncology trainees. The PHOELIX (Pediatric hematology/oncology educational laboratory in-training examination) was then developed to provide an annual formative evaluation of laboratory skills in Canadian PHO trainees. RESULTS: The majority of PHO respondents (89%) felt that laboratory skills are important in clinical practice. An annual formative examination including review of glass slides was implemented starting in 2010; this provides feedback regarding knowledge of laboratory medicine to both trainees and program directors (PDs). CONCLUSIONS: We have successfully created a formative examination that can be used to evaluate and educate trainees, as well as provide PDs with a tool to gauge the effectiveness of their laboratory training curriculum. Feedback has been positive from both trainees and PDs.

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)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.416
Teacher spread0.336 · 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 designObservational
DomainEvaluation
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
Published2015
Admission routes3
Has abstractyes

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