Outpatient Endocrine Diagnosis and Treatment of Thyroid Diseases and Disorders in a Portuguese Public University Hospital
Bibliographic record
Abstract
Background: Thyroid diseases and disorders account for most of the activity of outpatient endocrine departments, but objective data regarding the care of such patients is difficult to find. Knowledge of that data would be fundamental for the planning of those departments and to develop translational research. Methods: A specific database was defined using the Statistical Package for the Social Sciences Program (SPSS IBM, 24th version, 2017) to include the records of all patients assisted by one of us, between January 2005 and December 2016, at the outpatient endocrine department of a public university hospital. Clinical and analytical data regarding the first and last visits was included. Statistical analysis used the same software program. Results: A total of 1,222 patients were included. Diagnostic groups were: 1) Simple nodular goiter (SNG) (46%); 2) Hashimoto’s thyroiditis (HT) (29%); 3) Graves’ disease (GD) (10%); 4) Toxic nodular goiter (TNG) (7%); and 5) Thyroid neoplasia (TN) (5%). After a mean follow-up of 5 years most patients with GD (70%), TNG (77%), HT (67%) and TN (92%) but no patients with SNG (32%) had either received definitive treatment or were being treated. However the euthyroid state was far from universal and not significantly different across groups (62-86%). Conclusions: Continued specialist care of patients with thyroid diseases and disorders is far from perfect. It corrects thyroid dysfunction in most but not all patients and most patients remain on thyroid medications, despite definitive treatment in many. Long-term follow-up of these patients is probably the responsibility of the endocrine team. Otherwise major complications may come to the attention of the specialists only when late recognition is made by general physicians or internists. J Endocrinol Metab. 2019;9(1-2):3-17 doi: https://doi.org/10.14740/jem550
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".