Assessing Treatment Effects Using Quality of Life Questionnaires in Pregnant Women with Iron Deficiency Anaemia [26H]
Bibliographic record
Abstract
INTRODUCTION: There is considerable overlap between symptoms related to iron deficiency anaemia (IDA) and a normally advancing gestation, posing challenges to the use of quality of life (QoL) tools to measure treatment effects. Our aim was to determine how best to use two existing QoL tools; short-form 36 (SF36) – a general questionnaire covering eight domains and the multidimensional fatigue symptom inventory: short form (MFSI-SF) – a fatigue-specific questionnaire to assess treatment effect in pregnant women with IDA. METHODS: We recruited consecutive women with IDA attending the Obstetric Day Unit at Mount Sinai Hospital, Toronto, for intravenous iron infusions over six months. Consenting participants completed SF36 and MFSI-SF questionnaires at each visit and upon completion of treatment. Scores for the entire questionnaire and individual domains were summed and plotted graphically over time. RESULTS: 29 women consented and completed at least one SF36 and MFSI-SF questionnaire, while 24 completed two, 12 completed three and four women completed four. The mean maternal age was 34 (24-46) years and the mean gestational age 29 (25-38) weeks. These women represented different ethnic groups and socio-economic strata. Although there was no consistent pattern in scores for 6/8 SF36 domains, there was a significant improvement between visits in the energy/fatigue (37.6 vs 42.8, p=0.044), and emotional wellness (62.7 vs 77.3, p<0.001) domains and in MFSI-SF scores (17.1 vs 12.2, p=0.025). CONCLUSION: When assessing treatment effects in pregnant women with IDA, consideration should be given to using QoL tools specifically designed to measure fatigue, such as MFSI-SF or the energy/fatigue domain in SF36.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".