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Record W2262546126

Quality of thyroid referrals in Saskatchewan.

2013· article· en· W2262546126 on OpenAlexaffabout
Kerollos Nashat Wanis, Jennifer J. Oucharek, Gary Groot

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineThyroid nodulesThyroidReferralNodule (geology)MalignancyGeneral surgeryThyroid cancerPediatricsRadiologyInternal medicineFamily medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: A thyroid nodule is a common presentation for thyroid pathology. A low proportion of thyroid nodules harbour malignancy and the investigation of these nodules should be performed in a cost-effective manner. The American Thyroid Association (ATA) has published guidelines which should aid physicians in performing the appropriate investigations. AIM: To determine the proportion of patients referred to thyroid surgeons in Saskatchewan with appropriate pre-referral work-up. METHODS: Data were retrospectively collected from the charts of all new thyroid referrals seen between 8 June 2011 and 8 June 2012 by two thyroid surgeons in the Saskatoon Health Region, Saskatchewan, Canada. Main outcome measures were the presence of thyroid stimulating hormone (TSH) and ultrasound results, and the appropriateness of ultrasound report recommendations in referrals to thyroid surgeons. RESULTS: Recent TSH results were done and sent to the thyroid surgeon in 55.1% of referrals. A recent ultrasound was performed in 92.3% of referrals. Of patients with a high or normal TSH, a radionuclide scan was inappropriately recommended in 11.5% of cases. CONCLUSION: There is room for improvement in pre-referral work-up of patients with thyroid nodules in Saskatchewan, in order to facilitate appropriate clinical decision making in a cost-effective manner.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.053
GPT teacher head0.292
Teacher spread0.240 · 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

Citations4
Published2013
Admission routes2
Has abstractyes

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