Authors' reply: value of fluorodeoxyglucose positron emission tomography in women with breast cancer (Br J Surg 2005; 92: 1363-1367)
Bibliographic record
Abstract
Sir Being well aware of the intrinsic limitations of FDG-PET in breast cancer, we do not advocate its use as a screening technique. The aim of our study was to evaluate the role of FDG-PET in the detection of distant metastases in women with primary or recurrent breast cancer. As the study was intended as a pilot, we are aware that it was underpowered to draw definite conclusions. However, we do not share the opinion of Hayanga that FDG-PET is too expensive to use routinely; it is not a question of cost, but of cost-effectiveness. This requires careful assessment of the new technology, including optimal patient selection and rigorous evaluation of all effects (medical, social and economic). A recent Canadian meta-analysis concluded that the use of a PET management strategy for the staging of breast cancer is expected to remain economically viable in Canada1. The statement that detection of tumours below 1 cm in diameter is beyond the resolution capabilities of FDG-PET is not completely correct. Metabolic activity rather than size determines FDG-PET positivity. Very large (usually benign or very well-differentiated cancers) may be PET-negative, while metabolically active lesions of just a few mm may be depicted by FDG-PET. We do not share the view of Hayanga on the use of other tracers like FLT. The fact that FLT depicts proliferation and thus may be more tumour-specific does not at all predict sensitivity. For staging purposes, FDG-PET is very sensitive, while FLT-PET depicts a completely different molecular feature of cancer (i.e. proliferation), which may not only aid in differentiation between viable tumour and inflammatory changes, yet may also be of prognostic significance2. Hayanga cites Fueger et al. to underline the limitations of FDG-PET and later advocates PET-CT, but these authors only observed a marginal improvement of PET-CT over PET alone3. Indeed, monitoring of response to neoadjuvant chemotherapy is an interesting and rapidly evolving application of FDG-PET. Intuitively, the use of an early, functional marker of response is very attractive. However, the present study does not address this topic and the final conclusion of Hayanga remains highly speculative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".