Authors’ Response: Dosage Recommendations for Combination Regimen of Thyroxine and 3,5,3′-Triiodothyronine
Bibliographic record
Abstract
Dr. Blanchard commented (1) that our comparison of therapy with T4 to combination therapy with T3 and T4 (2) may have tested an inappropriate dosage of T4 and T3. This is based on his anecdotal experience with 1000 patients on combination therapy of T4 and T3 for whom the ratio of T4:T3 was 98%:2%. His patients were apparently “much better” (1) on combination therapy than on their previous therapy. We agree with Dr. Blanchard that our findings may not be generalizable to other dosing regimens of combination T4/T3 therapy or to a sustained release of T3 preparation. However, if it is possible that different combination therapies are superior, this should be rigorously tested within a double-blinded, randomized, controlled trial setting. Of note, the psychological scores [Symptom Checklist-90 (SCL-90) and Center for Epidemiological Studies-Depression Scale (CES-D)] in our study improved in both treatment groups with repeated testing; this observation suggests that there was a substantial placebo effect (2). It is well established that anecdotal experience, without a control group, may be subject to various biases that may overestimate benefits (3). In this light, a multicenter, randomized, controlled trial that explicitly tests the benefits and risks of alternative dosing regimens of combination T3/T4 therapy would clearly be appropriate. In the meantime, the best controlled evidence does not support changing the way that hypothyroidism is routinely treated.
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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 | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.033 | 0.027 |
| Insufficient payload (model declined to judge) | 0.024 | 0.024 |
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".