The (commercialised) experience of operating: Embodied preferences, ambiguous variations and explaining widespread patient harm
Bibliographic record
Abstract
This article provides a detailed account of how surgeons perceived and used a device-procedure that caused widespread patient harm: transvaginal mesh for the treatment of pelvic floor disorders in women. Drawing from interviews with 27 surgeons in Canada, the UK, the United States and France and observations of major international medical conferences in North America and Europe between 2015 and 2018, we describe the commercially driven array of operative variations in the use of transvaginal mesh and show that surgeons' understanding of their hands-on, sensory experience with these variations is central to explaining patient harm. Surgeons often developed preferences for how to manage actual and anticipated dangers of transvaginal mesh procedures through embodied operative adjustments, but collectively the meaning of these preferences was fragmented, contested and deferred. We critically reflect on surgeons' understandings of their operative experience, including the view that such experience is not evidence. The harm in this case poses a challenge to some ways of thinking about uncertainty and errors in medical sociology, and calls for attention to a specific feature of surgical work: the extent and persistence of operative practices that elude classification as right or wrong but are still most certainly better and worse.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.051 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".