Abstract: Carpal Tunnel Syndrome Management in Breast Cancer Survivors at Risk for Lymphedema: A Markov Model
Bibliographic record
Abstract
INTRODUCTION: Breast cancer (BC) survivors that have had an axillary lymph node dissection (ALND) have an increased risk of developing upper extremity lymphedema. The problem faced by both patients and clinicians is the decision to proceed with a carpal tunnel release (CTR) if the patient does not respond to non-surgical management. The purpose of this study was to determine the treatment decision that yields the highest quality adjusted life years (QALY) for BC survivors at risk for lymphedema presenting with carpal tunnel syndrome (CTS). METHODS: A state transition Markov cohort model was used to evaluate the treatment options for BC survivors at risk of upper extremity lymphedema presenting with CTS, as this allowed weighing the advantages and disadvantages of performing CTR or continuing with non-surgical management. The model reflected three treatment strategies: 1) early surgical intervention (mild CTS), 2) delayed surgical intervention (severe CTS), or 3) non-surgical management. QALYs for each strategy were calculated over a lifetime time horizon. RESULTS: Over a lifetime (30-year) horizon, the preferred strategy was delayed surgery, which resulted in 21.41 QALYs. Early surgery and non-surgical management yielded 20.42 and 21.06 QALYs, respectively. The model was not sensitive to variation in any of the parameters within the clinically plausible ranges. CONCLUSION: Based on this robust decision analytic model, BC survivors with mild CTS who are at risk for lymphedema would gain the most QALY by delaying CTR until severe CTS develops. This strategy balances the increased risk of lymphedema following CTR to the decreased long-term risk of severe CTS. The model comprehensively assesses a controversial area in the BC and hand surgery literature in order to guide decision making for patients and clinicians.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".