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Record W2804955523 · doi:10.1002/hed.25204

Use of radioiodine‐131 scan to measure influence of surgical discipline, practice, and volume on residual thyroid tissue after total thyroidectomy for differentiated thyroid carcinoma

2018· article· en· W2804955523 on OpenAlexaff
Jin Soo Song, Nico Moolman, Steven Burrell, Murali Rajaraman, Martin Bullock, Jonathan Trites, S. Mark Taylor, Matthew H. Rigby, Robert D. Hart

Bibliographic record

VenueHead & Neck · 2018
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineThyroidThyroidectomyThyroid carcinomaOtorhinolaryngologyNuclear medicineRadioactive iodineAblationPapillary carcinomaRadiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Our study's purpose is to determine the influence of surgical discipline, surgeon site, and volume on remnant thyroid tissue visualized on radioactive iodine-131 (I-131) scans after total thyroidectomy and I-131 ablation in patients with well-differentiated thyroid carcinomas. METHODS: We retrospectively reviewed all cases of patients who received I-131 therapeutic ablation and postablation radioactive I-131 scans at our center after thyroidectomy to calculate the fraction of administered dose multiplied by 1000 (UDR1000). RESULTS: The remnant thyroid tissue (ie, the UDR1000), between academic and community surgeons was 0.471 (±0.705) and 1.190 (±2.487), respectively (P = .001). The UDR1000 between otolaryngology-head and neck surgery and general surgery was 0.654 (±1.575) and 1.043 (±1.625), respectively (P = .159). The UDR1000 partitioned by patient frequencies of <10, 10 to 19, and ≥20 patients yielded 1.255 (±2.554), 0.926 (±2.084), and 0.467 (±0.721), respectively (P = .003). CONCLUSION: Our study found statistically significant differences in residual thyroid tissue visualized on radioactive I-131 scans based on surgeon parameters.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.024
GPT teacher head0.306
Teacher spread0.282 · 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 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

Citations6
Published2018
Admission routes1
Has abstractyes

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