The psychosocial impact of an abnormal cervical smear result
Bibliographic record
Abstract
BACKGROUND: Data on the impact of abnormal cervical smear results on health-related quality of life (HrQoL) are scarce. We aimed to (i) prospectively assess the HrQoL of women who were informed of an abnormal smear result; (ii) identify predictors of greater negative psychosocial impact of an abnormal result; and (iii) prospectively estimate the quality-adjusted life-years (QALYs) lost following an abnormal result. METHODS: Between 08/2006 and 08/2008, 492 women with an abnormal result and 460 women with a normal result, frequency matched for age and clinic, were recruited across Canada. HrQoL was measured at recruitment and 4 and 12 weeks later with the EuroQol, Short Form-12, short Spielberg State-Trait Anxiety Inventory (STAI) and HPV Impact Profile. Three blocks of potential predictors of higher psychosocial impact were tested by hierarchical modeling: (i) socio-demographics; (ii) sexual activity; and (iii) smear result severity, communication, and understanding. RESULTS: Receiving an abnormal result significantly increased anxiety (STAI mean difference between both groups = 8.3). Initial anxiety decreased over time for the majority of women. However, 35% of women had clinically meaningful anxiety at 12 weeks (i.e. STAI scores ≥0.5 standard deviation of the controls). These women reported a lower socio-economic level, did not completely understand the information about their result and perceived themselves at higher risk of cancer. QALY lost following an abnormal result were between 0.007 and 0.009. CONCLUSIONS: Receiving an abnormal smear has a statistically significant and clinically meaningful negative impact on mental health. However, this negative impact subsides after 12 weeks for the majority of women.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".