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TRUVACE RECORD VERSION
record: TRV-2026-2879
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-13T00:02:48.506991Z
status: published
lens: p_space
sector: health
headline: Performance of a Large Language Model for Predicting Clinical and Radiographic Outcomes in Knee Osteoarthritis: Single-Center Prospective Observational Study
dek: Background: Large language models (LLMs) can extract information from unstructured clinical text, but it remains unclear whether their ability to assess current status translates into reliable predictions of longitudinal outcomes.
Objective: This study aimed to evaluate DeepSeek's ability to assess baseline clinical and radiographic status in knee osteoarthritis (KOA), its performance in predicting 6-month outcomes, and its interrun reproducibility.
Methods: This study used a single-center, prospective observati…
gain_title: (none)
problem_title: In a prospective study of 111 knee osteoarthritis patients, DeepSeek's 6-month WOMAC score predictions erred by 5.92 to 6.58 points on average, versus 3.86 for a rheumatologist and 5.43 for a simple no-change model.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: In a prospective study of 111 knee osteoarthritis patients, DeepSeek's 6-month WOMAC score predictions erred by 5.92 to 6.58 points on average, versus 3.86 for a rheumatologist and 5.43 for a simple no-change model.
problem_evidence: 6-month predictions (WOMAC mean absolute error=5.92-6.58; KL exact agreement=74/111, 66.7%-76/111, 68.5%) were worse than those of the physician comparator (mean absolute error=3.86; 96/111, 86.5%) and not superior to those of the persistence model (mean absolute error=5.43)
quick_read: A single-center study in knee osteoarthritis tested whether the DeepSeek-V4-Flash language model could predict how patients would be doing six months later. Of 128 enrolled patients, 111 completed follow-up. The model's WOMAC pain-and-function score predictions were off by 5.92 to 6.58 points on average, compared with 3.86 for a rheumatologist.

The model also matched the physician's baseline diagnosis in 105 of 111 patients (94.6%), so reading current status from interview records and imaging reports worked far better than forecasting. Its forecasts did not beat a persistence model that simply assumes no change (error 5.43), which limits the case for using it to predict outcomes.

Results were also unstable at patient level. Three independent runs gave highly correlated scores (intraclass correlation 0.982 and 0.965), yet exact agreement between runs was only 31.5% and 6.3% on the two measures. The authors conclude that prognostic uses need evidence of value over a persistence model.
limitation: The study was single-center, with 111 patients analysed, and evaluated only one DeepSeek configuration, so results may not carry over to other settings or models.
tag: Evidence-backed problem
key_points: 128 patients with primary knee osteoarthritis were enrolled; 111 (86.7%) completed the 6-month follow-up and were analysed. | DeepSeek-V4-Flash and a rheumatologist each assessed diagnosis, WOMAC scores and Kellgren-Lawrence grades from the same interview records and imaging reports. | Baseline diagnosis agreed with the physician in 105 of 111 patients (94.6%), with good agreement on WOMAC and KL grading. | Six-month Kellgren-Lawrence exact agreement was 66.7% to 68.5% for the model, versus 86.5% (96 of 111) for the physician. | The model's WOMAC error of 5.92 to 6.58 compared with 5.43 for the persistence model, so it was not superior. | The model was run three times; exact agreement between runs was 31.5% on one measure and 6.3% on the other.
rundown: The study used a single-center, prospective observational cohort with a blinded, retrospective evaluation of the language model. Both DeepSeek-V4-Flash and a rheumatologist worked from the same standardized interview records and imaging reports, while a separate senior physician determined the actual outcomes used as the reference.

The comparison had three arms: the model, the physician and a persistence model that assumes no change. The physician had the lowest WOMAC error (3.86) and highest Kellgren-Lawrence exact agreement (86.5%), while the model's errors were higher than the persistence model's.

Reproducibility was mixed. Intraclass correlation coefficients across three runs were high (0.982 and 0.965), but patient-level exact agreement between runs was 31.5% and 6.3%. The authors say the configuration may support structured baseline assessment, while its predictive accuracy and stability remain limited.
sources:
- peer_reviewed | JMIR Medical Informatics | https://doi.org/10.2196/107092 | 2026-11-12
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