Agreement Between Older Subjects and Proxy Informants on History of Surgery and Childbirth
Bibliographic record
Abstract
OBJECTIVES: To assess the agreement between proxy informants' reports of history of surgery and childbirth and older index subjects' own recall. DESIGN: Interrater reliability study. SETTING: An outpatient family medicine clinic and a provincial electoral district in Montreal, Canada. PARTICIPANTS: Eighty-two subjects aged 65 years and older without cognitive impairment, identified from clinic and community settings, and each index subject's proxy respondent. MEASUREMENTS: Identical questionnaires were administered to index subjects and proxies. RESULTS: Proxies failed to report 39% of non-childbirth surgeries reported by index subjects, but failed to report only 10% of childbirths. Female proxies were significantly less likely than male proxies to underreport non-childbirth surgeries after controlling for age of index subject and interval since surgery. Longer interval since surgery was significantly associated with greater underreporting, whereas age of the index subject and relationship between proxy and index subject were not. Agreement between proxies and index subjects on date of surgery was much higher for childbirths than for non-childbirth surgeries. CONCLUSIONS: Our findings suggest that proxy respondents can provide reliable information on older women's history of childbirth but that use of proxy respondents for history of non-childbirth surgeries may result in substantial underreporting.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".