Characterization of hyperlipidemia secondary to mitotane in adrenocortical carcinoma
Bibliographic record
Abstract
Background This study examined the magnitude of changes and the time required to observe maximal changes in LDL-c, HDL-c, triglycerides (Tg) and non-HDL-c after the introduction of mitotane. Methods Retrospective study of 45 patients with adrenocortical carcinoma who were treated at the Centre hospitalier de l’Université de Montréal. Clinical and biochemical data were collected, including lipid profiles before and during the first year of treatment with mitotane. Results Among the 45 studied patients, 26 (58%) had a complete lipid profile before the introduction of mitotane and at least 1 lipid profile during the first year of treatment, and 19 patients (42%) had a lipid profile following initiation of the treatment. Among the 26 patients who had lipid profiles before and after the introduction of mitotane, the increase of LDL-c was 2.19 mmol/L (76%) ( P < 0.0001), HDL-c was 0.54 mmol/L (35%) ( P = 0.0002), Tg was 1.80 mmol/L (129%) ( P < 0.0001) and non-HDL-c was 2.73 mmol/L (79%) ( P < 0.0001). Between the first and the sixth month of mitotane treatment, peak values ( n = 45) of LDL-c and non-HDL-c were reached in 42 patients (93%) and 37 patients (82%), respectively, whereas peak values of HDL-c were reached after 6 months of mitotane treatment in 29 patients (66%). The peak value of Tg was almost equal throughout the first year. The mean peak values of HDL-c, Tg and non-HDL-c showed significant associations with their respective mitotane concentrations (β = 0.352, P = 0.03; β = 0.406, P = 0.02 and β = 0.339, P = 0.05). Conclusion The introduction of mitotane produces a clinically significant elevation of lipid parameters (LDL-c, HDL-c, Tg and non-HDL-c) during the first year of treatment.
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 | 0.000 | 0.001 |
| 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".