Pain Assessment Using Virtual Reality Facemask During Bone Marrow Aspiration: Prospective Study Including Propensity-Matched Analysis
Bibliographic record
Abstract
BACKGROUND: Bone marrow aspiration (BMA) is a medical procedure necessary to the diagnosis and monitoring of patients with hematological or nonhematological disorders. This procedure is considered painful, and patients are generally anxious before and during BMA. OBJECTIVE: This study assesses the effect of immersive virtual reality on pain during BMA. METHODS: This observational prospective and monocentric study enrolled 105 consecutive patients who underwent sternal BMA with lidocaine anesthesia. The study was carried on during 2 periods. First, virtual reality facemask (VRF) was proposed to all patients in the absence of exclusion criteria. During the second period, BMA was performed without the VRF. For all patients, pain intensity after the procedure was assessed using a 10-point numerical pain rating scale (NPRS). All analyses were performed on propensity score-matched cohort (with or without VRF) to evaluate efficacy on NRPS levels. RESULTS: The final matched cohort included 12 patients in the VRF group and 24 in the control group. No difference in anxiety level before BMA evaluated by the patient and by the operator was observed between groups (P=.71 and .42 respectively). No difference of NPRS was observed using VRF when compared to control group (median NPRS 3.8, IQR 2.0-6.3 vs 3.0, IQR 1.9-3.0, respectively; P=.09). CONCLUSIONS: Our study did not prove the efficacy of VRF to reduce pain during BMA.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| 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".