Proofmark paraphrases your academic manuscript and holds every number, citation, cross-reference and modal verb exactly where it was - then proves it with a measured report you can hand to a reviewer.
AI-assisted editing. We say so on every file we return - and you should say so to your journal.
Before
Machine learning1 of 11
Before
The preprocessing steps were applied in a fixed order: standardisation, then oversampling, then windowing. Oversampling must never be applied before the train-test split, because doing so allows synthetic neighbours of a test record to enter the training partition.
After
Preprocessing ran in a fixed order - standardisation first, then oversampling, then windowing. Applying oversampling before the train-test split is something that must never happen, since it lets synthetic neighbours of a test record slip into the training partition.
✓ order kept✓ modal kept✓ terms kept
Clinical medicine2 of 11
Before
After a median follow-up of 31 months, patients randomised to early anticoagulation had a lower incidence of the composite primary endpoint than those assigned to delayed treatment (hazard ratio 0.78, 95% CI 0.64 to 0.95; p = 0.013). The reduction was smaller among participants aged 75 years or older, which may partly reflect the higher rate of major bleeding in that subgroup (Table 3).
After
After a median of 31 months of follow-up, the composite primary endpoint occurred less often among participants randomised to early anticoagulation than among those allocated to delayed treatment (p = 0.013); the hazard ratio was 0.78 (95% CI 0.64 to 0.95). In patients aged 75 years and over, the reduction was less marked, which may in part reflect the greater frequency of major bleeding in this subgroup (Table 3).
✓ numbers kept✓ hedge kept✓ cross-ref kept
Public health3 of 11
Before
Between 2019 and 2023, childhood obesity prevalence fell by 4.2 percentage points in districts that adopted the sugar-sweetened beverage levy, against 1.6 points in districts that did not (Fig. 2). This difference is consistent with a protective effect of the levy but does not establish one: adoption was voluntary rather than randomised, and only 14 of the 61 districts supplied complete follow-up data.
After
In districts adopting the sugar-sweetened beverage levy, childhood obesity prevalence dropped by 4.2 percentage points from 2019 to 2023, compared with 1.6 points in non-adopting districts (Fig. 2). Although consistent with the levy having a protective effect, the difference does not establish that such an effect exists, because adoption was left to each district instead of being randomised, and only 14 districts out of 61 returned complete follow-up data.
✓ hedge kept✓ negation kept✓ quantifier kept
Materials engineering4 of 11
Before
After 28 days of curing at 5 °C, the fly ash specimens reached a mean compressive strength of 31.2 MPa, 14% lower than the companion set cured at 20 °C. Chloride diffusivity, by contrast, did not differ significantly between the two regimes. The strength deficit may stem from a slower pozzolanic reaction at low temperature rather than from any change in porosity.
After
For the fly ash specimens cured at 5 °C, mean compressive strength after 28 days was 31.2 MPa, a value 14% lower than that of the companion specimens cured at 20 °C. In contrast, chloride diffusivity showed no significant difference between the two regimes. This shortfall in strength may be due to the pozzolanic reaction proceeding more slowly under low-temperature curing, rather than to any change in porosity.
✓ numbers kept✓ direction kept✓ hedge kept
Computer science5 of 11
Before
As shown in Table 4, the pruned model retained 97.8% of the baseline accuracy while cutting inference latency from 41 ms to 26 ms per batch. These gains held for all three datasets but did not carry over to the quantised variant described in Algorithm 2. We therefore follow the calibration procedure of [12] rather than the per-layer scheme proposed in [7].
After
Table 4 shows that the pruned model preserved 97.8% of baseline accuracy while reducing per-batch inference latency to 26 ms, down from 41 ms. The gains held across all three datasets, yet they did not extend to the quantised variant outlined in Algorithm 2. For this reason, we calibrate using the procedure of [12] instead of the per-layer scheme put forward in [7].
✓ cross-ref kept✓ citation kept✓ quantifier kept
Psychology6 of 11
Before
Consistent with earlier work on attentional bias (Okafor et al., 2021), most participants in the high-anxiety group fixated on threat-related faces within the first 500 ms of each trial. However, this pattern emerged only when stimuli were presented peripherally; with central presentation, fixation latencies did not differ between groups, suggesting that spatial uncertainty may be a necessary condition for the bias.
After
In the high-anxiety group, most participants fixated threat-related faces during the initial 500 ms of every trial, a finding in line with earlier attentional bias research (Okafor et al., 2021). The pattern was observed, however, only when the stimuli appeared in the periphery; with centrally presented stimuli, the groups showed no difference in fixation latency, which suggests that the bias may require spatial uncertainty.
✓ citation kept✓ quantifier kept✓ hedge kept
Economics7 of 11
Before
Following the 2019 childcare reform, the share of mothers with children under five working full time rose by 2.4 percentage points (Table 3). The rise appears to stem mainly from part-time employees extending their hours, rather than from inactive mothers entering work: transitions out of inactivity did not increase relative to the pre-reform trend in any of the 12 quarters examined.
After
Among mothers of under-fives, the proportion in full-time work increased by 2.4 percentage points after the 2019 childcare reform (Table 3). This increase appears to be driven chiefly by part-time employees lengthening their hours rather than by inactive mothers taking up employment: in none of the 12 quarters studied did transitions out of inactivity rise above the pre-reform trend.
✓ numbers kept✓ hedge kept✓ direction kept
Chemistry8 of 11
Before
The crude ester was first dissolved in anhydrous toluene and stirred at 65 °C for 2 h under nitrogen. The mixture was then cooled to 0 °C, after which triethylamine (1.2 equiv.) was added dropwise over 15 min. Finally, the solids were removed by filtration and the filtrate was concentrated under reduced pressure, giving the product in 81% yield (Table 2).
After
First, the crude ester was taken up in anhydrous toluene, and the solution was stirred under nitrogen for 2 h at 65 °C. The mixture was next cooled to 0 °C before triethylamine (1.2 equiv.) was introduced dropwise over 15 min. Lastly, the solids were filtered off and the filtrate concentrated under reduced pressure, which gave an 81% yield of the product (Table 2).
✓ order kept✓ numbers kept✓ cross-ref kept
Ecology9 of 11
Before
Restoring hedgerows along field margins may improve pollinator visitation to adjacent crops, although the effect was detected at only 9 of the 14 sites surveyed. A higher bee count near a restored margin does not imply that pollination services increased, since visitation rates were not paired with fruit-set measurements (Lindqvist and Mbeki, 2019). Further seasons of monitoring will be needed before any yield benefit can be claimed.
After
Hedgerow restoration on field margins may improve the rate at which pollinators visit neighbouring crops, though the effect was observed at only 9 sites out of the 14 surveyed. Finding more bees close to restored margins does not imply an increase in pollination services, because no fruit-set measurements were paired with the visitation rates (Lindqvist and Mbeki, 2019). Claiming any yield benefit will require additional seasons of monitoring.
✓ hedge kept✓ negation kept✓ quantifier kept
Statistics10 of 11
Before
Under the conditions of Lemma 3, the estimator in Eq. (9a) is unbiased if and only if the sampling weights sum to one within each stratum. When this constraint is relaxed, the bias grows linearly with the stratum imbalance, and the simulations in Table 4 suggest that it may exceed 0.15 standard errors once fewer than 40 units are drawn per stratum.
After
Under the hypotheses of Lemma 3, unbiasedness of the estimator given by Eq. (9a) holds if and only if every stratum's sampling weights add up to one. If the constraint is relaxed, bias rises linearly as stratum imbalance increases, and the Table 4 simulations suggest it may surpass 0.15 standard errors when the number of units sampled per stratum falls below 40.
✓ cross-ref kept✓ hedge kept✓ numbers kept
Education research11 of 11
Before
Teachers in the intervention schools completed at least 12 hours of coaching before the spring assessment window, whereas comparison teachers received none. Because coaching intensity varied considerably across the 18 participating schools, the pooled effect of 0.21 standard deviations should not be read as the impact a single school could expect; site-level estimates, reported in Table 3, ranged from 0.02 to 0.38.
After
Teachers at intervention schools were coached for at least 12 hours ahead of the spring assessment window; teachers in the comparison group, by contrast, received no coaching. Coaching intensity differed substantially among the 18 schools that took part. The pooled effect (0.21 standard deviations) therefore should not be taken as the impact an individual school could expect. As Table 3 shows, site-level estimates varied from 0.02 to 0.38.
✓ modal kept✓ quantifier kept✓ numbers kept
Watch it work
From your Word file to a measured report.
1Sign in with Google or with your email address.
2Upload your manuscript as a Word .docx file.
3See what will be paraphrased and what is kept as is, and add your keywords.
4Check the price, then pay - or use a credit if you have one.
5Each paragraph is rewritten and checked while you wait.
6Read the measured report, then download the clean .docx, the Track Changes copy and the change log.
No sound. Six real screens from the site, step by step.
100
pages per pass, up to a full thesis - quoted before you pay
0
numbers, citations or cross-references lost - checked one by one
.docx
back in your own file - tables, figures and styles intact
How it works
Four steps. Nothing leaves unmeasured.
01 - Upload
Drop your Word file
We read it in your browser session, sort every paragraph - prose, heading, caption, table, reference, equation - and show you exactly what will be touched. Nothing is sent anywhere yet.
02 - Quote
See the word split, then pay
Total words, words in scope, and the size and price that fits them. Untick any paragraph you want left alone. You agree to the scope before you pay for it.
03 - Rewrite and measure
Every paragraph, then every check
Each paragraph is rewritten with its own list of what must survive. What comes back is diffed against that list and measured for how far it moved. Anything too close goes straight back.
04 - Review and download
Read every paragraph side by side
Source beside rewrite, one row per paragraph, worst overlap first. Download the clean file and the change log that says what moved and what did not.
What we protect
A rewrite can keep every word and still change the claim.
A must that drifts onto a different clause. A rather than whose second half quietly disappears. A before that becomes an after. These are the errors a fluent paraphrase makes, and a reviewer catches. Before a single sentence is rewritten, we inventory everything that must survive - then check it survived.
And we never touch the title, author line, headings, captions, tables, reference list or equations. Those are yours.
Numbers, with their direction
3.4× slower stays slower. 41,092 retained is never dropped.
Citations, frozen in place
[7] and (Saeb et al., 2015) are masked before the model ever sees them.
Cross-references
Table 4, Section 3.5, Eq. (9a) - present, and pointing at the same thing.
Modal verbs, bound to their clause
must never becomes should, and never migrates to a different sentence.
Procedural order
in a fixed order: A, then B does not become A and B. In a methods section the order is the method.
Negations and antitheses
consistent with X but does not establish X keeps its second half.
Hedging, in both directions
may improve is never promoted to improves, and does not establish is never softened to does not imply.
Your keywords
Paste the words and phrases that must not change - base rate, held-out set - and we hold them.
The report
Every file comes back with its numbers.
This is a real report from a 5,461-word methods paper. We never report “N paragraphs changed” - a single swapped connective satisfies that. We report how much of your original wording is still standing, and whether the pass cleared its own bar.
Measure
Result
Target
Whole document, 8-word overlap
12.3%
under 25%
✓
Median paragraph
7.2%
under 15%
✓
Worst paragraph
37.9%
none above 50%
✓
Word count
3,502 → 3,689
within ±10%
✓
Numbers, citations, cross-refs lost
0
0
✓
Paragraphs
523 in, 523 out
none merged
✓
Also in every change log
The paragraphs that stayed closest to the source, named rather than averaged away - usually a caption, or a sentence built around a protected antithesis that can only move so far.
Every check that flagged something, with whether it turned out to be a real defect or a false alarm. We investigate each one before it reaches you.
Anything the author needs to decide - a repair we made, a phrase we restored, a paragraph we left untouched and why.
Pricing
Per manuscript. Quoted before you pay.
Short paper
Price outside Iraq: $56,000 IQDup to 8 pages (2,400 words in scope)
Clean file, tracked-changes copy and change log. If the pass misses its own targets, an editor finishes it by hand.
You never pick a size: we count the words that will be paraphrased - at 300 words to a page - and quote the smallest size that fits. References, tables, captions and headings are not counted.
What Proofmark is not
It is not a way to hide that a text was written with help, and nothing here is built to get a text past a detector. Our report measures how far a rewrite moved from its source and whether every claim survived; it says nothing about who wrote what. Most journals now ask authors to declare AI-assisted editing. Declare it - and attach our change log if they want to know exactly what changed.