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Thin layer compared to direct smear in thyroid fine needle aspiration

2000· article· en· W1985264326 on OpenAlexaffabout
James Scurry, Máire A. Duggan

Bibliographic record

VenueCytopathology · 2000
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
Fundersnot available
KeywordsMedicineThyroidFine-needle aspirationRadiologyInternal medicineBiopsy

Abstract

fetched live from OpenAlex

The efficacy of preparing thyroid fine needle aspirations (FNAs) by the thin layer as opposed to the direct smear method has not been evaluated sufficiently in a regional laboratory setting. At the Foothills Hospital (Calgary, Canada), the method of processing thyroid FNAs was changed from direct smear to thin layer in January 1996. The results of 327 patients who had direct smear from 1994 to 1995 were compared to 401 who had thin layer between 1996 and 1997. While there were no significant differences across a broad range of quality indicators, thin layer showed a trend towards a higher proportion of true benign diagnoses (31% vs 24%), a lower proportion of inadequate specimens (41% vs 50%) and, most importantly, a lower false negative rate (3% vs 9%). In conclusion, the changeover to thin layer did not compromise the interpretation of thyroid FNAs.

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)0.007
metaresearch head score (Gemma)0.017
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.291
Teacher spread0.266 · 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

Citations67
Published2000
Admission routes2
Has abstractyes

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