Bibliographic record
Abstract
AIM: To understand why doctors differ in their recommendations in situations where there is little certainty about the long term outcomes of the possible treatment options. METHODS: A correlational design was used to examine the relation between preference for different treatment options and beliefs about likely outcomes for these options. Eighty doctors, with a mean of nine years in paediatric cardiology/surgery, attending a conference on serious congenital heart disease were studied. Main outcome measures were: ratings of the extent to which each of four treatment options were favoured; and subjective probabilities for three outcomes-death, survival with "good heart function" (New York Heart Association functional class (NYHA) I or II), and survival with "poor heart function" (NYHA III or IV)-for different treatment options over a 20 year time frame. RESULTS: Preference for one treatment option over another was most closely associated with the subjective estimate of the additional years with "good heart function" that it offered 10-20 years after surgery (Pearson's r = 0.66, p < 0.001). In influencing a preference, the possibility of early death was subordinate to optimising the late outcome. CONCLUSIONS: Doctors' treatment preferences are consistent with selecting the option that maximises the chance of the best outcome (long term survival with good heart function). Doctors' recommendations imply that they place more value on years of life in the child's far future than on life-years in the immediate future.
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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 | 0.012 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".