Using the CUSUM Test to Control the Proportion of Inadequate Open Biopsies of Musculoskeletal Tumors
Bibliographic record
Abstract
BACKGROUND: Biopsies of musculoskeletal tumors lead to alterations in treatment in almost 20% of cases. Control charts are useful to ensure that a process is operating at a predetermined level of performance, although their use has not been demonstrated in assessing the adequacy of musculoskeletal biopsies. QUESTIONS/PURPOSES: We therefore (1) assessed the incidence of and the reasons for inadequate musculoskeletal biopsies when following guidelines for performing the procedure; and (2) implemented a process control chart, the CUSUM test, to monitor the proportion of inadequate biopsies. METHODS: We prospectively studied 116 incisional biopsies. The biopsy was performed according to 10 rules to (1) minimize contamination in the tissues surrounding the tumor; and (2) improve accuracy. A frozen section was systematically performed to confirm that a representative specimen was obtained. Procedures were considered inadequate if: (1) another biopsy was necessary; (2) the biopsy tract was not appropriately placed; and (3) the treatment provided based on the diagnosis from the biopsy was not appropriate. RESULTS: Five (4.3%) of the 116 incisional biopsy procedures were considered failures. Three patients required a second repeat open biopsy and two were considered to receive inappropriate treatment. No alarm was raised by the control chart and the performance was deemed adequate over the monitoring period. CONCLUSIONS: The proportion of inadequate musculoskeletal open biopsies performed at a referral center was low. Using a statistical process control method to monitor the failures provided a continuous measure of the performance.
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".