Establishing Reference Values for Health Related Quality of Life Scores During Pregnancy [16M]
Bibliographic record
Abstract
INTRODUCTION: This study aims to generate gestational-age specific Health-Related Quality of Life (HRQoL) scores from a diverse population of low-risk pregnant women, to provide reference values for future research and clinical practice. METHODS: We conducted a prospective longitudinal study at Mount Sinai Hospital in Toronto, Canada. Over a period of three months, we recruited 333 women over the age of 18 with low-risk singleton pregnancies between 12 and 40 weeks of gestation. Participants completed a demographic survey at the first visit and two HRQoL questionnaires - the Short Form-36 (SF-36) and the Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF) at each visit. The SF-36 was composed of eight domains: physical functioning, role limitations due to physical health, role limitations due to emotional health, energy/fatigue, emotional well-being, social functioning, pain and general health scores. The MFSI-SF produced domains consisting of the general score and the vigor score. Gestational-age-specific mean HRQoL scores and standard deviations were calculated to determine how they changed throughout the course of pregnancy in various domains. Approved by Mount Sinai Hospital Research Ethics Board. RESULTS: Although SF36 scores were constant for the general domain, those for the energy domain as well as MFSI-SF scores tended to rise from 12 weeks and peak at 20-27 weeks before dropping slightly again and plateauing between 34 and 40 weeks. CONCLUSION: HRQoL scores, especially those related to energy and vigor, fluctuate during the course of pregnancy and these fluctuations need to be considered when comparing the effect of interventions or the progression of disease conditions during pregnancy.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".