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Record W4281644083 · doi:10.1007/s40120-022-00366-4

Therapeutic Decision-Making Under Uncertainty in the Management of Spinal Muscular Atrophy: Results From DECISIONS-SMA Study

2022· article· en· W4281644083 on OpenAlexaff
Gustavo Saposnik, Ana Camacho, Paola Díaz-Abós, María Brañas-Pampillón, Victoria Sánchez-Menéndez, Rosana Cabello-Moruno, María Terzaghi, Jorge Mauriño, Ignacio Málaga

Bibliographic record

VenueNeurology and Therapy · 2022
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoSt. Michael's Hospital
FundersRoche
KeywordsSpinal muscular atrophySMA*MedicineNeurologyPhysical medicine and rehabilitationNeurosciencePhysical therapyPathologyPsychologyComputer scienceDiseasePsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: There are many uncertainties about treatment selection and expectations regarding therapeutic goals and benefits in the new landscape of spinal muscular atrophy (SMA). Our aim was to assess treatment preferences and expectations of pediatric neurologists caring for patients with SMA. METHODS: DECISIONS-SMA is a non-interventional, cross-sectional pilot study that assessed pediatric neurologists with expertise in SMA from across Spain. Participants were presented with 11 simulated case scenarios of common encounters of patients with SMA type 1 and 2 to assess treatment initiation, escalation, or switches. We also asked for the expected benefit with new therapies for four simulated case scenarios. Participants completed a behavioral battery to address their tolerance to uncertainty and aversion to ambiguity. The primary outcome was therapeutic inertia (TI), defined as the number of simulated scenarios with lack of treatment initiation or escalation when warranted over the total (11) presented cases. RESULTS: A total of 35 participants completed the study. Participants' mean (SD) expectation for achieving an improvement by starting a new therapy for SMA type 1 (case 1, a 5-month-old) and SMA type 2 (case 6, a 1-year-old) were both 59.6% (± 21.8), but declined to 20.2% (± 12.2) for a case scenario of a 16-year-old treatment-naïve patient with long-standing SMA type 2 with severe disability. The mean (SD) TI score was 4.2 (1.7), and 3.29 (1.5) for treatment initiation. Of a total 385 individual responses, TI was observed in 147 (38.2%) of treatment choices. The multivariable analysis showed that lower aversion to ambiguity (p = 0.019) and lower expectation of treatment response (p = 0.007) were associated with higher TI after adjustment for participants' age and years of experience. Older age (p = 0.019), lower years of experience (p = 0.035), lower aversion to ambiguity (p = 0.015), and lower expectation of treatment benefits (p = 0.006) were associated with inertia for treatment initiation. CONCLUSIONS: Pediatric neurologists managing patients with SMA were optimistic regarding treatment improvement in cases with early diagnosis, but had lower expectations when treatment delays and advanced patient age were present. Low aversion to ambiguity, low expectation of treatment benefits, and lower clinical experience were more likely to make suboptimal decisions, resulting in lack of treatment initiation, escalation, and TI.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.346
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations10
Published2022
Admission routes1
Has abstractyes

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