The association between STOPPFall medication use and orthostatic hypotension in community-dwelling older people
Bibliographic record
Abstract
INTRODUCTION: Fall-risk-increasing drugs (FRIDs), identified by the Screening Tool of Older Persons Prescriptions in older adults with high fall risk (STOPPFall), may increase fall risk by causing orthostatic hypotension (OH).This study investigates the association between STOPPFall medication use and OH in community-dwelling older people ≥65 years using data from The Irish Longitudinal Study on Ageing (TILDA). METHODS: Orthostatic blood pressure (BP) was measured by active stand using a Finometer. STOPPFall medications were recorded at TILDA Waves 1 & 3.Delayed BP recovery was defined by a reduction in systolic BP (sBP) ≥20 mmHg and/or diastolic BP ≥ 10 mmHg from baseline at 30 seconds after standing, 'any OH' a similar BP reduction at either 30, 60, 90 or 120 seconds post-stand, and classical OH by persistent reduction in sBP at all timepoints (30-120 seconds) post-stand.Regression models assessed the association between STOPPFall medications and OH and orthostatic sBP changes. RESULTS: One STOPPFall medication was prescribed in 26.9% (403/1499) of participants; 10.6% (159/1499) were prescribed ≥2 STOPPFall medications.Prescription of ≥2 STOPPFall medications was independently associated with delayed BP recovery [odds ratio (OR) 1.88 (95% CI 1.25-2.82); P = .003], 'any OH' [OR 1.48 (95% CI 1.00-2.18); P = .048], and classical OH [OR 2.07 (95% CI 1.13-3.78); P = .018], and was associated with significantly lower sBP at 30- [coefficient -7.91(95% CI -10.22 to -5.60); P < .001], 60- [coefficient -5.55(95% CI -7.86 to -3.24); P < .001], 90- [coefficient -2.63(95% CI -4.95 to -0.32); P = .026], and 120 seconds [coefficient - 3.67(95% CI -5.98 to -1.36); P = .002].Increasing STOPPFall medication at Wave 3 was associated with significantly lower sBP at 30 seconds [coefficient -3.22(95% CI -5.73 to -0.72); P = .012]. CONCLUSION: Prescription of ≥2 STOPPFall medications was associated with significantly delayed BP recovery post-stand and OH. This highlights that rationalising STOPPFall medications is indicated in older people with OH.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".