Predicting Calcium Status Post Thyroidectomy with Early Calcium Levels
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.
Bibliographic record
Abstract
OBJECTIVE: The study goals were to predict postoperative normocalcemia and hypocalcemia after total thyroidectomy using calcium levels and to assess the value of a standardized protocol in managing the total thyroidectomy patient. STUDY DESIGN: We conducted a prospective study encompassing 68 patients undergoing a total thyroidectomy using a standardized protocol. Blood to measure postoperative calcium levels was drawn at 6, 12, and 20 hours and then twice daily thereafter. Calcium slope was calculated from the 6- and 12-hour serum corrected calcium levels. RESULTS: Logistic regression analysis allowed the comparison of the 6- and 12-hour calcium slope versus proportion of normocalcemic patients postoperatively. A slope of +0.02 had a 97% chance of remaining normocalcemic (p = 0.0007). CONCLUSION: Successful prediction of calcium status post total thyroidectomy can be achieved using the slope of the 6- and 12-hour calcium levels. The risk of developing severe hypocalcemia can also be predicted with these slope values. Implementation of the protocol resulted in a significant reduction in the duration of hospital stay for patients who remain normocalcemic.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it