Bibliographic record
Abstract
Nausea and vomiting, especially during the early stages of pregnancy, is a common problem among patients of family physicians. Good treatments are available, but sometimes they do not work, and all require medications. Because they fear teratogenicity, some patients want to avoid traditional antiemetics. Complementary or alternative therapies can off er options to ease unpleasant and potentially dangerous nausea and vomiting. After hearing about magnets being used successfully for motion sickness and seeing the results on a family member, I tried using magnets for a pregnant patient. Th is patient had presented at 13 weeks’ gestation with uncontrolled nausea and vomiting despite having taken full doses of doxylamine succinate–pyridoxine hydrochloride (Diclectin®) since 9 weeks’ gestation. She estimated she was retaining only 500 mL/d of fl uid (she has some paramedical training). I off ered her intravenous treatment in hospital or multipolar magnets on the insides of both wrists with close follow up. She chose the latter, and I contacted her three times a day for the following 2 days. She had no more vomiting on the day of application and “mild vomiting the next morning” (her words). She did not vomit again until the seventh morning when she returned to “violent vomiting” (her words). She had not put the magnets on following her shower the night before. She completed the pregnancy using the magnets with no further concerns. Few patients have nausea and vomiting this severe during pregnancy, but I did have another patient whom I treated successfully with the same method.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".