Parathyroid hormone‐driven algorithms after thyroid surgery: Not one‐size‐fits‐all
Bibliographic record
Abstract
BACKGROUND: Underreported variation in parathyroid hormone (PTH) assays exists. Using quality improvement methods, we aimed to develop an institution-specific PTH-based protocol to predict hypocalcemia after thyroidectomy. METHODS: We retrospectively reviewed patients who underwent total/completion thyroidectomy. A receiver operating curve (ROC) determined postoperative PTH cut-offs predictive of hypocalcemia. The stakeholders developed PTH-driven calcium management guidelines. Post-implementation outcomes were prospectively measured. RESULTS: Pre-implementation, 95 patients were assessed. PTH ≤1.5 pmol/L (14.1 pg/ml) predicted hypocalcemia (96%sensitivity), and ≥2.8 pmol/L (26.4 pg/ml) predicted normocalcemia (99%specificity) (area under curve = 0.97, SEM = 0.018). PTH on the day of and morning after surgery were identically predictive. Post-implementation, 64 patients were assessed. Hypocalcemia occurred with PTH >2.8 pmol/L in 2 cases (3.1%). Calcium over-prescribing decreased from 13.7% to 3.1% (p = 0.06). Length of stay (LOS) > 2 nights decreased from 13% to 3.1% (p = 0.05). CONCLUSION: A PTH-driven calcium management protocol post-thyroidectomy effectively reduces unnecessary calcium replacement and LOS. Given the variability in PTH assays, each institution may need to use individual cut-offs.
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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".