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Record W4211175816 · doi:10.1111/jgs.17681

Recovery of driving fitness after stroke: A matter of time?

2022· letter· en· W4211175816 on OpenAlexaboutno aff
Philipp Schulz, Wolf‐Ruediger Schaebitz, Martin Drießen, Thomas Beblo, Max Toepper

Bibliographic record

VenueJournal of the American Geriatrics Society · 2022
Typeletter
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)Physical medicine and rehabilitationRehabilitationInjury preventionMeta-analysisPoison controlHuman factors and ergonomicsCognitionPhysical therapyMedical emergencyPsychiatryInternal medicine

Abstract

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Driving safety is often significantly reduced after stroke due to an increased risk of reinfarction. As the rate of recurrent strokes significantly decreases after time intervals of three and six months poststroke,1 physicians usually recommend driving breaks of three or six months, respectively. Besides the risk of reinfarction, however, driving safety is reduced due to functional deficits caused by the stroke. Both factors limit mobility in stroke survivors. Since mobility is crucial for an independent life, it is not surprising that more than half of the patients consider the recovery of driving fitness being a key target of stroke rehabilitation.2 To be able to specify when driving safety is back to normal again, it is relevant to know whether the length of a driving break is actually related to driving fitness. To our knowledge, studies investigating primarily the relation between the length of a driving break and on-road driving performance in stroke survivors are completely missing. Since time plays a crucial role in the recovery of driving-relevant cognitive, sensory, and motor functions, however, it is reasonable to assume that on-road-driving fitness recovers accordingly. In 2011, Devos and colleagues published a systematic review and meta-analysis.3 This study included on-road studies assessing fitness to drive in patients after stroke. Up to now, there is no new meta-analytical on-road data for stroke survivors, except a review article4 that included no new on-road studies. Although the data presented by Devos and colleagues show substantial heterogeneity, they indicate that across several studies 46% of stroke survivors do not pass an on-road driving assessment. Given that only 13% who return to driving have undergone a formal driving assessment beforehand,3 these results suggest that there may be many stroke survivors who resume driving too early and despite impaired fitness to drive. Data on the relation between the lengths of driving breaks and on-road driving performances that may have allowed more specific assumptions were not presented in the Devos article. For this reason, we reanalyzed the relationship between on-road pass rates and time since stroke onset of the studies in the Devos article.3 In 18 out of 30 studies, information about the number of stroke survivors who passed an on-road driving assessment and the corresponding time interval between stroke onset and on-road assessment was given. One study was excluded from data analysis,5 since this study differed from the others regarding on-road pass criteria. Eventually, 17 studies were included in the analyses. The mean age across those studies was about 60 years with an age range between 16 and 85 years (Table 1). We calculated pass rates per study and correlated them with the number of months between stroke onset and on-road driving assessment. To account for methodological quality of the studies, we additionally calculated a partial correlation by controlling for the methodological quality of the studies as rated by two independent reviewers using the Newcastle-Ottawa Scale (compare Table 1 in the Devos article). Figure 1 shows the result of the correlation analysis revealing a moderate positive correlation (Pearson correlation) between the months since stroke onset and the observed rate of stroke survivors who definitely passed the on-road driving assessment (r = 0.40, p = 0.11, n = 17). After controlling for methodological quality of the studies, the effect size increased to rp = 0.50 (p = 0.05). As expected, these findings suggest increasing on-road pass rates of stroke survivors with increasing time between stroke onset and on-road driving assessment. In sum, the results confirm that driving-relevant cognitive, sensory, and motor functions recover over time and support the appropriateness of driving breaks after stroke. However, even though the rate of impaired drivers appears to decrease over time, there remains a relevant number of stroke survivors failing an on-road driving assessment even after longer breaks. Moreover, Figure 1 shows considerable variance between studies regarding pass rates even at similar time frames, probably due to differences in stroke severity, etiology, and localization. Overall, the data do not allow the definition of a generally valid length of a driving break that guarantees driving safety poststroke. When it is safe to drive again, should rather be a person-centered decision based upon a formal on-road driving evaluation. In conclusion, more longitudinal on-road studies with comparable methodologies are urgently needed to be able to define the lengths of driving breaks dependent on stroke type, etiology and severity, and under consideration of individual functional deficits, recovery, and the risk of reinfarction.6 Moreover, those studies should focus on advanced ages since the current sample was relatively young. Open Access funding enabled and organized by Projekt DEAL. The authors declare that there is no conflict of interest. Everyone who contributed significantly to the work is listed. All authors meet the criteria for authorship stated in the Uniform Requirements for Manuscripts Submitted to Biomedical Journals. Max Toepper and Philipp Schulz analyzed the data. Max Toepper, Philipp Schulz, Thomas Beblo, Wolf-Rüdiger Schaebitz, and Martin Driessen interpreted the results and wrote the article by drafting it between the authors. There is no funding to report for this submission.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.510
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.015
GPT teacher head0.312
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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