Surgeon predictions on growth of minimal invasive therapy: the difficulty of estimating technologic diffusion
Bibliographic record
Abstract
OBJECTIVE: To compare five-year predictions made in 1992 by academic surgeon leaders in UK, US and Canada, with actual experiences in 1997, of increased rates of minimal invasive therapy (MIT) for surgical operations. METHOD: We compared 1992 predictions of percent of operations done by minimal invasive therapy and length of stay in the US with actual 1997 percents found by literature searches. RESULTS: We found sufficient data on 12 operations done by MIT in 1997 of the original 34 operations predicted in 1992 by surgeon experts to be to be amenable to this technique. These 12 operations were among the top 20 most commonly performed procedures in 1992 and 1997. Of these 12 operations, ten had 40-60% lower 1997 percentages than predicted, one had about 10% lower rate, and two had 18% and 100% higher rates of MIT than predicted. Overall mean length of stay (LOS) for all 34 study operations fell from 6.8 days in 1992 to 5.2 days in 1997. Mean LOS in 1997 was 2.5 days by MIT and 6.7 days by open technique (OT). CONCLUSION: Most of the predictions made in 1992 by surgical leaders in Canada, US and UK were incorrect when examined 5 years later. The rate of MIT diffusion and its effect on length of stay were overestimated for most operations, while for two procedures the predictions underestimated extent of diffusion. Also, much of the declines of LOS for surgical care paralleled declines in length of stay for all care, supplemented by the individual contributions of MIT specifically. Relying on expert opinion alone to predict the acceptability, rapidity, scope and extent of technological change is fraught with uncertainty. Unexpected consequences occur when one or a few parts of complex systems are changed. This is a particular problem when predictions are a main basis for informed decision making in the absence of any supporting data from appropriately designed empirical or controlled study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".