MétaCan
Menu
Back to cohort

Thin layer compared to direct smear in thyroid fine needle aspiration

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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