Ablative free thyroxine to thyroglobulin ratio as a predictor of differentiated thyroid cancer recurrence.
Bibliographic record
Abstract
BACKGROUND: Serum thyroglobulin (Tg), a widely used thyroid cancer marker, is limited at the time of ablation, unable to differentiate between diseased and normal residual tissue. OBJECTIVE: We evaluated the use of the ablation free thyroxine to thyroglobulin ratio (fT4:Tg) as a tumour-specific ratio for predicting persistence or recurrence in differentiated thyroid cancer. DESIGN: Retrospective chart review. SETTING: McGill University Health Centre. METHODS: Of 234 patients, 84 were analyzed after exclusion of those with anti-Tg antibodies, ablation Tg < or = 2, and follow-up < 3 months. Ablation thyroxine and Tg levels were recorded and patients were followed to detect recurrence. The relationship between the ablation fT4:thyroglobulin ratio and recurrence was evaluated. MAIN OUTCOME MEASURES: Hazards ratio (HR) for predictive fT4:Tg ratio cutoff value and disease-free survival based on the fT4:Tg ratio. RESULTS: Thirty-eight percent of patients developed recurrence: 8 pathologically proven and 24 suspected. Eighty-one percent of patients with recurrence had an fT4:Tg < 27%, in contrast to 23% of those without recurrence (HR 6.2; p < .001). Of all patients with fT4:Tg < 27%, 68% developed evidence of recurrence compared with 13% with fT4:Tg > or = 27% (p < .001). Recurrences in the fT4:Tg < 27% group occurred twice as early. CONCLUSION: Ablation fT4:Tg < 27% is predictive of recurrence and should be used to identify high-risk patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".