1 The Motherisk Program, The Hospital for Sick Children, Toronto ON
Bibliographic record
Abstract
Background: Most women worry to some extent during pregnancy about exposure to agents that might harm their babies. Cases: We describe three women who worried excessively throughout pregnancy about harming their babies because of exposure to agents including, but not limited to, psychotropic drugs. These women were extremely resistant to reassurances that their babies would not be adversely affected, and it is likely there are more women in the community who fit this profile. We have described a number of management strategies that we found effective in caring for these women during pregnancy. Conclusion: A collaborative effort between caregivers in psychiatry and obstetrics, as well as other health professionals, is required to provide management for these women during pregnancy. Résumé Contexte: La plupart des femmes présentent au cours de la grossesse, dans une certaine mesure, des inquiétudes au sujet de l’exposition à des agents pouvant nuire au fœtus. Cas: Nous avons décrit la situation de trois femmes qui ont présenté des inquiétudes excessives, tout au long de la grossesse, quant à la possibilité de porter tort à leur fœtus en raison d’une exposition à des agents, dont (entre autres) les psychotropes. Ces femmes se sont avérées extrêmement résistantes aux formules de réconfort affirmant que leur fœtus ne subirait pas de conséquences indésirables; de plus, il est probable qu’un nombre important de femmes correspondent à ce profil au sein de la communauté. Nous avons décrit un certain nombre de stratégies s’étant avérées efficaces pour la prise en charge de ces femmes au cours de la grossesse.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.511 | 0.089 |
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".