Impact of Cosmobiological Factors on Chromosomal Anomalies Rates - Ethical Consideration
Bibliographic record
Abstract
Backround: In recent years, there is an increasing number of data supporting the relation between lunar cycle and alterations in human body. For instance, it has been proved that hospitalized spontaneous abortions are significantly associated with the lunar cycle . Given that the most common cause of spontaneous abortion is chromosomal abnormalities of the embryo, we presumed that the lunar cycle could influence the occurrence of chromosomal diseases. From the point of bioethics it is a question if it is applicable to talk with patients about possible impact of cosmobiological factors on their case of chromosomal anomaly. Patients and Methods: We have selected 52 patients, who were bearing a fetus with a prenatally diagnosed chromosomal disorder, which included Down, Turner, Klinefelter, Patau and Edward syndromes. The control group (n=92) consisted of families, that were prenatally tested for chromosomal abnormalities and the results were negative. Then we gathered and analyzed information from patients' medical histories about various factors, which could have predisposed chromosomal disorders: age of both progenitors, consumption of medications during pregnancy, number of miscarriages, number of abortions, cases of genetic disorders in relatives, lunar phase on the day of conception. Results: Lunar phases at the time of conception in the test group distributed as following: new Moon 32.7 percent, first quarter 25.0 percent, full Moon 28.8 percent, third quarter 13.5 percent. In the control group lunar phases distributed as following: new Moon 29.3 percent, first quarter 27.2 percent, full Moon 26.1 percent, third quarter 17.4 percent. There was observed no statistically significant difference between test and control groups (p>0.05). OR for conception with chromosomal anomalies were increased in July (2.7(CI 95% 1.5-4.9) and December (3.2 (CI 95% 1.5-5.2) (p<0.05). OR showed decreased risk for chromosomal anomalies during conception at January (0.3 (CI 95% 0.1-0.7) , April (0.5 (CI 95% 0.2-0.7) and June 0.5 (CI 95% 0.2-0.8) (p<0.05). Conclusions: Our study showed there were no associations between lunar phase at the time of conception with the rates of chromosomal mutations. Conception in July and December increases the risk of chromosomal anomalies during conception. In contrast conception in January, April and June decreases chromosomal anomalies rates.
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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 | 0.021 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".